Global supply chains face increasing challenges in improving efficiency, transparency, and compliance with regulatory requirements. Traditional supply chain systems often suffer from inefficiencies due to fragmented data and manual processes, which result in delays and higher costs. Blockchain technology has emerged as a potential solution by offering decentralization, data immutability, and automation through smart contracts. However, existing blockchain implementations struggle with issues like scalability and transaction speed, which limits their effectiveness in supply chain management. This study introduces a new framework based on distributed ledger technology (DLT) with enhanced smart contract functions and data provenance tracking. The framework aims to improve transaction throughput, reduce latency, and provide better data integrity, enabling more efficient and transparent supply chain operations. By incorporating mechanisms to track the origin and movement of goods, the framework ensures that stakeholders have real-time access to accurate information, improving decision-making and trust across the supply chain. We evaluate the performance of this framework using the AnyLogic simulation platform, comparing it to traditional blockchain systems. Metrics such as transaction throughput, latency, and efficiency are analyzed to demonstrate the improvements achieved by the proposed system. The results show significant enhancements in transaction speed and operational efficiency, offering a practical solution for optimizing supply chains in various industries.

Pinus gerardiana (Family-Pinaceae) known as the “chilgoza” which is native to the northwestern Himalayas. There are about 29 species of pine and are utilized by indigenous tribal cultures in the world. In India, out of six species of pine, Pinus gerardiana is the only species which produces edible and highly nutritious nuts. The FAO reported that the utilization of pine nuts as a comestible food item was practiced from pre-historic times to date. Its oil is of very good quality, free of cholesterol and a rich source of fatty acids like Stearic acid (0.3%), Linoleic acid (51.3 %), Linolenic acid (1.5%), Oleic acid (39.7%), Arachidic acid (2.1%), Palmitic acid (7.2%).The extraction of pine nut oil was done using oil extracting machine in locally available small-scale industry and the extracted Pine nut oil is used to cook various recipes like Veg Pulao, Green gram sandal, Cutlet and Vegetable salad and were randomly evaluated using five-point hedonic scale and the Nutritive analysis was done laboratorically. The result showed that Veg pulao, cutlet, green gram sundal and vegetable salad prepared using pine nut oil is rich in Fat (23.31gms, 16.42 gms, 15.4 gms, 7.4gms) respectively and Protein (12.7 gms, 13.2 gms, 14.8 gms, 3 gms) respectively. It is concluded that fusion of pine nut oil in the different recipes were found to be rich in all essential fatty acids, proteins, several vitamins and minerals and were acceptable among the adolescence girls. It also helps to prevent many diseases from its occurrence.

Millions of individuals worldwide are afflicted by the complex and deadly disease of cancer. The key
to enhancing patient outcomes and raising survival rates is early identification. The development of
precise and effective techniques for cancer diagnosis has advanced significantly since the invention of machine learning. This study presents a thorough analysis of various machine learning approaches and their applications in the detection of cancer, highlighting their advantages, drawbacks, and potential future prospects. With millions of deaths annually, cancer remains one of the greatest threats to world health. The best way to increase patient outcomes and survival rates is through early identification.
The application ofmachine learning techniques to oncology has demonstrated impressive potential in
recent yearsfor improving cancer detection and diagnosis. Researchers and healthcare professionals
are making ground-breaking progress in detecting cancer in its early stages, enabling prompt
intervention and individualized treatment programs. They are doing this by using the power ofartificial
intelligence and sophisticated algorithms.

The primary objective of this study is to analyze the myriad factors influencing students' academic performance and forecast their performance for the upcoming semester using historical performance data. Identifying students falling below Institutional performance standards and furnishing them with tailored support for academic improvement is pivotal. This research endeavors to unearth and elucidate potential variables impacting both student performance and Institutional outcomes. Additionally, it aims to scrutinize correlations among specific attributes and students' academic achievements. To realize these objectives, a predictive model has been formulated, foreseeing students' future performance based on chosen attributes. The efficacy of this predictive model is demonstrated through the application of insights garnered from the analysis.

Intelligent agent-based systems have become a growing approach to decision-making in the business
applications. Analyzing with the maximum knowledge enriches the rapid and accurate decisions in the competitive business environment. With the explosive development and growth of accessing information from the internet makes integration of multiple database data from diverse locations gives voluminous database and constructive effects in the decision making. Distributed data mining can accomplish a mining task with databases in diverse locations. Instead of moving of agents to all locations moving of agents to maximum density data locations is timeconsuming, space consuming and also increase of accuracy in decision making. This paper focuses on thecomparative analysis of using intelligent-agent for distributed data mining and for density estimation based location selection for distributed data mining.

Medical domain research is significant to the society and human values. This reduces the guess works and improves decision making with the pattern generated from the history of patient information. The complexity of medical domain and manual analysis and extracting precise and accurate pattern of decision is a life-challenging task. Diagnosing with the maximum knowledge enriches the rapid and accurate decisions. Though human decision making is best, it is poor when there are huge amounts of data to be classified. In many cases the symptoms are more prone to many of the diseases. Predicting the level of infection is a most significant task for finding the
accurate therapy and early prevention of human life. Fuzzy Logic (FL) consists of probabilistic logic, which infers the rule set for classifying the medical data with fractional values of accuracy.

This paper express about the future innovative technologies of AI with real time example of AI robot-
Sophia. Some of the technology of AI is examined in day-to-day aspect. AI robotic plays an extra ordinary role and interacts with the peoples. Future view of AI also discussed AI system have involved in regular use economic, medical, engineering and the military , video games and so on.

Collection is realization a tipping point for main technical changes that can fetch new ways in
decision making, managing our health, cities, finance and education. whilst the data complexities are increasing counting data’s volume, variety, velocity and veracity, the real impact pivots on our ability to uncover the value’ in the data through Big Data Analytics technologies. Big Data Analytics pose a impressive challenge on Big data is a new driver of the world economic and public changes. The world’s data the design of highly scalable algorithms and systems to integrate the data and uncover large hidden values from datasets that are diverse, complex, and of a massive scale. Potential breakthrough include new algorithms, methodologies,systems and applications in Big Data Analytics that discover useful and hidden knowledge from the Big Data competently and effectively. Big Data Analytics is relevant to Hong Kong as it move towards a digital financial system and society. Hong Kong is already among the best in the world in Big Data Analytics, taking up such management position as chairs and editor in chiefs of imperative conference and journal in Big Data linked
areas.

The network technology is developing fast, and the development of internet technology is more
quickly, people more aware of the importance of the network and its security. This Network security is main issue of computing because many types of attacks and problems are increasing day to day. In the mobile ad-hoc network the nodes are autonomous. Shielding computer and the network security are the critical issues. The malicious codes create a problem in the network and damages the network. This malicious codes acts as selfishness, It can use the resources of other nodes and preserve the resources of its own. After analyzing the network information and its security elements privacy, integrity and availability, this paper describes the network security privacy vector, network security integrity vector and network security availability vector; also we present the major type of attacks in MANET and the another MANET issues.

This paper is part of the research to study and challenges of cyber security for smart devices at home. We have seen the development and demand for seamless interconnectivity of smart devices to provide various functionality and abilities to user. Current cyber security issues related to smart devices are discussed and analyzed. The paper begins with related background and motivation. Mobile malware has one of the mainissues in the smart device security. The mobile users can expect to see a striking increase in malware and notable advancements in malware-related attacks. We discuss and analyzed mobile malware in details and identified challenges and future trends in this area. Then we propose and discuss and integrated security solution for cyber security in mobile to tackle the issues.

Software testing is a process of finding errors while executing a program in the software. It is a important part and critical phase of any software development life cycle and aimed to evaluating the capability, quality or usability of a program. The software testing is to make aquality of service in efficient way. Software must be fully tested before it could be handled to the customer. Software testing is the main activity of validating and executing the software with the major purpose of find out the errors. The black box testing, white box testing and grey box testing is the very important testing techniques in software testing strategies. This paper executing the comparative study on the black box, white box and grey box testing techniques. The software bug is arising at the time of software testing. The bug report is giving knowledge about the software testing. In this paper the testing strategies, common errors and bug report have been described. Some typical latest
researches have been summarized.

The major issues and challenges in Wireless Ad-hoc Networks (WANETs) stem from their dynamic, decentralized, and resource-constrained nature, including dynamic topology, limited bandwidth, energy constraints, security vulnerabilities, and scalability issues, requiring innovative solutions for routing, security, and resource management. Ad hoc networks are a new wireless networking paradigm for mobile hosts. Unlike traditional mobile wireless networks, ad hoc networks do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. The military tactical and other security-sensitive operations are still the main applications of ad hoc networks, although there is a trend to adopt ad hoc networks for commercial uses due to their unique properties. One main challenge in design of these networks is their vulnerability to security attacks. In this paper, we study the threats an ad hoc network faces and the security goals to be achieved. We identify the new challenges and opportunities posed by this new networking environment and explore new approaches to secure its communication. In particular, we take advantage of the inherent redundancy in ad hoc networks — multiple routes between nodes to defend routing against denial of service attacks. We also use replication and new cryptographic schemes, such as threshold cryptography, to build a highly secure and highly available key management service, which forms the core of our security framework

Wireless Adhoc Networks (WANETs) with TCP/IP protocol architecture design, highlighting the need for robust security measures due to their inherent vulnerabilities, such as dynamic topology and lack of centralized control. Adhoc networks are self-configuring, temporary networks formed by mobile devices that communicate directly without relying on pre-existing infrastructure or centralized control. The TCP/IP protocol suite is widely used for internet communication and is also a standard for adhoc networks, enabling reliable data transmission. The dynamic nature of adhoc networks, where nodes move and network topology changes rapidly, poses challenges for traditional TCP/IP architecture, which is designed for static networks. Adhoc networks are vulnerable to various security threats, including eavesdropping, spoofing, and denial-of-service attacks, due to their decentralized nature and lack of centralized control.

This study investigates the impact of multimedia visuals on information retrieval, examining how different visual elements (e.g., images, videos, animations) influence user search behavior and the effectiveness of information retrieval techniques. We explore the challenges and opportunities presented by multimedia content in the context of information retrieval, focusing on the development and evaluation of advanced techniques for indexing, searching, and presenting multimedia information. The research methodology includes [mention specific methods, e.g., user studies, experimental design, data analysis] to assess the effectiveness of different approaches. Our findings reveal that [mention key findings, e.g., specific visual types enhance retrieval accuracy, certain techniques improve search efficiency, etc.], leading to recommendations for [mention practical implications, e.g., improved system design, enhanced user experience, etc.] in multimedia information retrieval systems.

The abstract could describe how GPS technology is used to track the location of vehicles, potentially including the hardware and software involved. It might discuss the advantages of using GPS for vehicle tracking, such as real-time location data, route optimization, and fleet management.
The abstract could also address the challenges of GPS vehicle tracking, such as signal interference, battery life, and data security. The abstract might explain how GPS technology can be used to detect and monitor ground movement during earthquakes. It could discuss the principles behind using GPS for earthquake detection, such as measuring changes in the position of GPS stations. The abstract might also address the limitations of using GPS for earthquake detection, such as the need for a dense network of GPS stations and the potential for false positives.

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Wireless sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Routing protocols in WSNs might differ depending on the application and network architecture. In this article we present a survey of state-of-the-art routing techniques in WSNs. We first outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flit, hierarchical, and location-based routing. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS-based, and coherent-based depending on the protocol operation. We study the design trade-offs between energy and communication overhead savings in every routing paradigm. We also highlight the advantages and performance issues of each routing technique. The article concludes with possible future research areas.

Ontological engineering has garnered increasing attention over the last few years, as researchers have recognized ontologies are not just for knowledge-based systems---all software needs models of the world, and hence can make use of ontologies at design time [1]. A recent survey of the field [4] suggests developers of practical AI systems may especially benefit from their use. This survey earmarked several application classes that benefit from using ontologies, including natural language processing, intelligent information retrieval (especially from the Internet), virtual organizations, and simulation and modeling.

This abstract likely explores how data mining techniques can be leveraged to improve various aspects of software engineering, such as identifying patterns in code, predicting defects, or optimizing development processes, leading to more efficient and effective software development.

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The presence of decision support systems plays a vital role in many situations like business
intelligence and medical solutions. There are many designs has been proposed earlier to support decision making and suffers with the problem of accuracy and time complexity. We propose a novel approach which uses Multi-Attribute Density Estimation technique (MADE) to choose the set of locations from where the data can be retrieved. The method uses various meta data which represents the availability of data in different locations of the network and based on the meta data, the method computes the MADE measure to choose most optimal locations. From identified locations, the method generates diagnosis patterns which contain various information about the medical history available in the location. The number of agent generation is performed according to the MADE factor and based on the patterns generated, the DSF (Decisive Support Factor) is computed which shows the possibility of the disease. The proposed method reduces the time complexity and improves the accuracy of decision making system.

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The innovation in the development of computer technology has led to the idea of human computer
interaction. A research experiment in human computer interaction involves the young age group of people that are educated and technically knowledgeable. This paper focuses on the mental model in Human Computer Interaction. There are different approaches of this review paper and one of them is highlightingcurrent approach. This paper describes human, computer interaction, components and emotional intelligence. The development and design of an automated system that perform such task is still being accomplished.

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The Medical industry research is significant to the society and human values. This reduces the guess works and improves decision making with the pattern generated from the history of patient information. The complication of medical domain and manual analysis and extracting precise and accurate pattern of decision is a life-challenging task. Diagnosing with the maximum knowledge enriches the quick and accurate decisions. Though human decision making is best, it is poor when there are huge amounts of data to be classified. In many cases the symptoms are more prone to many of the diseases. Predicting the level of disease is a most significant task for finding the accurate treatment and early prevention of human life. Fuzzy Logic (FL) consists of probabilistic logic, which infers the rule set for classifying the medical data with fractional values of accuracy. This classification supports for accurate prediction and controlling the disease syndrome

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Now a day the Biometric is becomes the most popular technique due to its liability. Because of
need of high security systems, People are also using the biometrics broadly. Another feature of
biometric is its efficiency. It is very easy to use and handle. Biometrics is the study of biology
and metrics, or measurement, especially for the purpose of identifying individuals based on
unique characteristics. Digital scanning, measurement, and matching tools are used to create
automated systems for verifying identity through the comparison of biometric samples.

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This paper focuses on support developing countries to strengthen their scientific and technological
capacities to move towards more sustainable patterns of consumption and production assuming
that developing countries lack scientific and technological capacities with respect to attaining
sustainable patterns of consumption and production. This assumption is applicable for Bangladesh.
The major reasons are growing demand for unhealthy food, luxurious commodities, gradual
depletion of natural gas, unplanned urbanization and industrialization, and increasing levels of
pollution and emission. Thus, the paper aims to promote consumption and production processes in
Bangladesh in a sustainable pattern by way of strengthening the current state of scientific and
technological capacities in terms of ICT. The paper analyses current practices of consumption,
production and underlying supply chain issues in Bangladesh and identifies challenges and
obstacles that need to be overcome in order to ensure sustainable patterns of consumption and
production in the country. This paper also highlights some of the latest development in ICT for
sustainability purposes and provides guidelines on how these can be applied in the context of
Bangladesh. Thus, the objectives of this paper are to:
1) Analyse current practices of consumption, production and supply chain issues in Bangladesh,
and identify challenges that need to be overcome in order to make them sustainable
2) Provide guidelines on the applications of ICT in overcoming these challenges

Morphology and properties of the samples are flest attributes of synthesis techniques and sintering temperature, In order to understand the effect of synthesis techniques and sintering temperature of TIGI (ZST) synthesized by sold state reaction (SS). The Hexagonal cryscal structure of the vathesated 25T is confirmed by powder X ray diffraction (PXRD) analysis. The bonding formation of metal xven and functional groups are studied by Fourier Transform Infra-red analysis (FT-IR). The phology and particle size of each of ZST is studied using scanning electron microscope, Particle aze is Anond to be in the range of 101.7 nm and 131.3 am for solid state reaction. The dielectric constant with tture of different frequency exhibited the large dielectric constant of 13000 wwds: ZST Nanoparticles, XRD, FTIR and SEM

Technology and digitalization are increasingly core subjects for researchers across disciplines, from economics and management to engineering and the humanities. Digital innovations, such as the Internet of Things, big data, cloud computing, artificial intelligence and various digital technology-based platforms, are influencing business venturing and changing the ways that extend beyond entrepreneurship and innovation practices to influence culture, politics and society. Social media platforms and digital communications have even restructured social relations. Yet little research has fully addressed the profound relationship between technology, entrepreneurship/innovation and social change. To correct this oversight, this Special Issue in Technovation is devoted to publishing original research that enriches our knowledge about the nature of digital technology and its influence on entrepreneurship and innovation in digital economies. We elaborate the emerging new business models, their antecedents, and their economic and social consequences. The issue also highlights key emerging research areas of digital technology-based entrepreneurship/innovation and articulates an agenda for future scholarship

Similar to the Internet several decades ago, Block chain technology is expected to become a
highly disruptive technology that will presumably impact society and economy alike. In this
paper we present various scenarios as to how Block chain might affect the future of work. We
build on Self-Determination Theory, which takes into account different types of human needs
and motivations, as a theoretical framework. We conducted 24 qualitative interviews with Block
chain experts and created three different scenarios that outline potential future developments.
The expertsâ?? opinions range from predicting no significant impact of Block chain on the work
environment toward substantial changes that can have both beneficial and adverse consequences
for the work force. In this chapter we detail the three scenarios and further illustrate Block
chainâ??s potential implications for basic human needs in the context of the future of work.

The two basic methods for choosing a feature subset are wrapper and filter based. The feature
subset selection algorithm functions as a wrapper around an induction algorithm in the wrapper
based approach, a supervised method. The feature subset selection algorithm's black box, the
induction algorithm, is essentially the classifier itself. The filter approach is an unsupervised
strategy that looks at the value of features extracted from the data without taking into account
how well the induction procedure performed. In this study, the effects of the feature subset
selection approaches on the classification performance of rule-based learning algorithms, i.e.,
C4.5, RIPPER, PART, BFTree were investigated. These algorithms are fast in case of wrapper
based approach. For various datasets, significant accuracy improvements were achieved with the
wrapper based feature subset selection method. Other algorithms like Multilayer Perceptron
(MLP) and Random Forests (RF) were also applied on the same datasets for the purpose of
accuracy comparison. These two algorithms were very inefficient in terms of time when they
were used in wrapper approach.

This is the world of information. Bug tracking is a system which is used to solve out any type of bugs in any software. It is mostly useful. The Bug Tracker can be made from any two types. The first one being the system side, the other being the services side for any software company. In this system we have design different types of user permission like developer, tester
having different rights to connect software. As software development is completed, the projects are goes in the next phase i.e. testing. The test engineers those are working in the same project are test the product (Software developed by developer) and if any bug are found they can log (Enter) that bug with his description in our software also set the priority for that bug with his description in our software also set the priority for that bug like high, medium, low, not reproducible. After the first cycle of bug tracking is completed the developer those are working in those projects can log in to system and get the bug list with priority. He can solve the bug and change the status of that bug in our software. The administrator or Project manager can get the idea of bug status, work status of both developer and tester and time span for that project. There is also one major part of user side to test the software called as UAT (User Accepting Testing). If support engineer or client faces any issue in the system after delivery they can also log in to system and put their issue regarding that project.

Bug tracking is an essential discipline in the domain of software engineering. It has far reaching effects on the system when effectively used. The information provided in terms of bugs and solutions in the bug reports can help software engineers to act on them quickly and ensure that they are either rectified or eliminated from the system. The bulk of information provided in the bug reports may cause problem to developers in ascertaining poorly designed information. Therefore the bug tracking systems are to be improved and follow certain standards. To overcome the problem, we propose four fundamental directions to enhancing effectiveness of bug tracking systems. To demonstrate the efficiency of the proposed directions, we develop a
prototype application that tracks bugs effectively by capturing essential information from users and help resolve bugs quickly.

ABSTRACT
Millions of individuals worldwide are afflicted by the complex and deadly disease of cancer. The key
to enhancing patient outcomes and raising survival rates is early identification. The development of
precise and effective techniques for cancer diagnosis has advanced significantly since the invention of
machine learning. This study presents a thorough analysis of various machine learning approaches and
their applications in the detection of cancer, highlighting their advantages, drawbacks, and potential
future prospects. With millions of deaths annually, cancer remains one of the greatest threats to world
health. The best way to increase patient outcomes and survival rates is through early identification.
The application of machine learning techniques to oncology has demonstrated impressive potential in
recent years for improving cancer detection and diagnosis. Researchers and healthcare professionals
are making ground-breaking progress in detecting cancer in its early stages, enabling prompt
intervention and individualized treatment programs. They are doing this by using the power of artificial
intelligence and sophisticated algorithms.
Millions of individuals worldwide are afflicted by the complex and deadly disease of cancer. The key
to enhancing patient outcomes and raising survival rates is early identification. The development of
precise and effective techniques for cancer diagnosis has advanced significantly since the invention of
machine learning. This study presents a thorough analysis of various machine learning approaches and
their applications in the detection of cancer, highlighting their advantages, drawbacks, and potential
future prospects. With millions of deaths annually, cancer remains one of the greatest threats to world
health. The best way to increase patient outcomes and survival rates is through early identification.
The application of machine learning techniques to oncology has demonstrated impressive potential in
recent years for improving cancer detection and diagnosis. Researchers and healthcare professionals
are making ground-breaking progress in detecting cancer in its early stages, enabling prompt
intervention and individualized treatment programs. They are doing this by using the power of artificial
intelligence and sophisticated algorithms.

Biometric information has been a broadly discussed topic in the past and it still is. Recent
discussions consist of proposals for storing biometric facts on passports. There are for example video shops
using fingerprints for authentication, an increasing number of laptops have a fingerprint sensor incorporated,
and banks are beginning to apply biometric facts as well. This paper discusses why authentication using
biometric information isn't always a not unusual standard up to now. it additionally analyses if the usage of
biometric records makes structures greater cozy and if it's far really worth spending more money on such
systems. the dialogue and evaluation in the paper ends in the conclusion that during most instances the trade
offs which want to be made aren't proper enough to remember biometric consumer authentication as a better
opportunity to standard strategies consisting of login/password authentication. on the cease the paper
provides a quick outlook about the destiny use of `biometric information. Human biometric traits which
include face, finger, iris scanning, voice, signature and other functions offer a dependable security degree.

Cardiovascular diseases are the leading cause of global mortality, necessitating efficient and early diagnostic tools to prevent adverse outcomes. Traditional diagnostic methods often require significant expert analysis and can be prone to human error or high cost. This research proposes an automated diagnostic system leveraging deep learning, specifically Convolutional Neural Networks (CNNs), to address these limitations.

ABSTRACT - Among the main causes of death in the modern world is heart disease, which is brought on by a
low heart rate. As a result, it is crucial to monitor heart health by spotting heart rate deviations early on, as this
makes it simpler to find and treat anomalies in the heart's function early on. The healthcare systems are
increasingly utilizing wearable monitoring systems, artificial intelligence (AI), and the Internet of Things (IoT)
to analyze vast amounts of health-related data for early and accurate disease diagnosis and detection,
individualized treatment, and prognosis evaluation. Then, it's critical to evaluate how well data analytics and
machine learning work. The study's findings indicate that by utilizing accelerometers together with data
analytics approaches, future HR can be predicted more precisely.

The purpose of this paper is to explore the application of Artificial Intelligence
(AI) in thecontext of recruitment and selection. Multiple case studies have been
examined to showcasethe use of AI tools in this area. Implementing AI in Human
Resource Management (HRM) offers several advantages, including enhanced efficiency
in the recruiting and selection processes,as wellas access to abroader pool of potential
candidates. One significant benefit of AI in HRM is its potential to reduce the influence
of subjective factors such as nepotism and favoritism in the hiring and selection of
personnel. AI-driven systems rely on data and algorithms, making decisions based on
objective criteria rather than personal biases. Further more,the integration of AI in HRM
can have appositive impact on employee development, retention, and the effective
utilization of time and resources. AI technology can assist inidentifying employee
training needs,improving career development opportunities, and optimizing work force
management. It is worth noting that AI in HRM is still an evolving field, and many
HRM applications havenot yet fully harnessed the capabilities of machine learning.
Some AI solutions may lack arobust scientific foundation. As a result, the widespread
adoption of AI in HRM is still in itsearlystages and has only affected a limited portion
of the population.

The Progressive growth of Artificial Intelligence(AI),the recent advancements in deep learning and applying the latest deep learning methods for medical imaging have become an active research area both in the medical industry and academia. Owing to the significant breakthroughs attained by Deep Learning Models, it is envisioned that the interaction between patients and AI-based medical systems will enable soon with high safety and convenience, such intelligent system will genuinely ameliorate patient healthcare. The area of deep learning in medical imaging is overviewed including various key technologies arise from the diverse medical imaging with deep learning applications, including medical image reconstruction, medical image enhancement, medical image classification. The challenges in medical data analysis such as unavailability of the annotated dataset, Privacy and legal issues, dedicated medical experts, and nonstandard data are demanding high research works on deep learning for effective medical imaging solutions. The Proposed model employs an Artificial Intelligence and Deep Learning Models to determine the most accurate way for using medical imaging by using a Massive-Training Artificial Neural Network(MTANN) and Convolutional Neural Network(CNN) Deep Learning Tool. The proposed work computes the analysis like parameters, nuclei detection and segmentation, disease grading, quantitative analysis of cells in live-cell imaging experiments, mutation identification and its pathway association, survival and disease outcome prediction using microscopy images.

Abstract
The rapid digitization of services has resulted in a massive growth of cyber threats across industries, making cybersecurity one of the most critical research areas of the 21st century. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of predicting, detecting, and mitigating cyberattacks in real-time. This chapter explores the role of AI and ML in cybersecurity by reviewing current literature, highlighting practical applications, analyzing limitations, and identifying future directions. The discussion demonstrates how AI-driven tools strengthen defense mechanisms through intelligent intrusion detection, malware classification, phishing prevention, and anomaly detection. However, challenges such as adversarial attacks, data imbalance, and ethical concerns persist. The chapter concludes by proposing innovative solutions, including Explainable AI, federated learning, and blockchain integration, which represent the future of secure and resilient cyber defense.

Password-based authentication and key distribution are important in today’s computing
environment. Since passwords are easy to remember for human users, the password-based system
is used widely. However, due to the fact that the passwords are chosen from small space, the
password-based schemes are more susceptible to various attacks including password guessing
attacks. Recently, Choe and Kim proposed a new password authentication scheme for keystroke
dynamics. However, in this paper, we cryptanalyze the Choe-Kim scheme and show it is vulnerable
to various types of attacks such as server-deception attacks, server-impersonation attacks and
password guessing attacks.

Artificial Intelligence (AI) has far surpassed the hype of block chain and quantum computing.
This is due to the fact that huge computing resources are easily available to the common man.
The developers now take advantage of this in creating new Machine Learning models and to re-
train the existing models for better performance and results. The easy availability of High
Performance Computing (HPC) has resulted in a sudden increased demand for IT professionals
having Machine Learning skills.

ABSTRACT
The broad objective of this study is the attacks, threats and vulnerabilities of cyber infrastructure, which includes hardware and software systems, networks, enterprise networks, intranets, and its use Cyber Intrusion. To achieve this objective, the paper attempts to explain the importance of the network intrusion and cyber theft. It also discusses, in vivid detail, the reasons for the accelerated spread of Cyber crimes. The paper also includes full description and definition of cyber security, its role Network intrusion and cyber identity theft plays in, reasons for the increase in discussion Cyber crimes and their impact. In closing the authors recommend some preventive measures and possible solutions to cyber security attacks, threats and vulnerabilities. The paper concludes that technology has a role to play in mitigating the impact of cyber attacks, the vulnerability remains with human behavior and psychological tendencies.

A Study on Attack, Threats, Vulnerability

Abstract:
The broad objective of this study is the attacks, threats and vulnerabilities
of cyber infrastructure, which includes hardware and software systems,
networks, enterprise networks, intranets, and its use Cyber Intrusion. To
achieve this objective, the paper attempts to explain the importance of G
in the network intrusion and cyber theft. It also discusses, in vivid detail,
the reasons for the accelerated spread of Cyber crimes. The paper also
includes full description and definition of cyber security, its role Network
intrusion and cyber identity theft plays in, reasons for the increase in
discussion Cyber crimes and their impact. In closing the authors
recommend some preventive measures and Possible solutions to cyber
security attacks, threats and vulnerabilities. The paper concludes that
While technology has a role to play in mitigating the impact of cyber
attacks, the vulnerability remains with human behavior and psychological
tendencies. While the literature supports the threats Psychological
vulnerability to cyber attacks Offers investment in organizational
education campaigns Optimism that cyber attacks can be mitigated.
Keywords: Cyber Attacks, Cyber Vulnerability, Threats, Warfare

Abstract
Artificial Intelligence (AI) is revolutionizing the transportation sector by enhancing efficiency, safety,
and sustainability across various transportation modes. AI applications in transportation range from
autonomous vehicles and smart traffic management systems to predictive maintenance and optimized
logistics. Self-driving cars and trucks, powered by AI algorithms and real-time data analysis, are
leading to safer roads, reduced human error, and improved traffic flow. AI-driven traffic control
systems optimize congestion and reduce wait times at intersections, while AI-powered predictive
maintenance systems help prevent vehicle breakdowns and infrastructure failures. In logistics, AI is
used for route optimization, fleet management, and efficient delivery services, lowering operational
costs and carbon emissions. Additionally, AI enhances public transportation systems, making them
more responsive to passenger needs and improving scheduling accuracy. The integration of AI in
maritime, aviation, and rail transport further increases operational efficiency, safety, and
environmental sustainability. As AI continues to evolve, its applications in transportation are expected
to play a pivotal role in shaping smarter, greener, and more connected urban mobility systems.

ChatGPT is an advanced natural language processing (NLP) model developed by Open AI,
specifically belonging to the GPT (Generative Pre-trained Transformer) family. This model excels in
generating human-like text responses in a conversational context. Trained on diverse datasets,
ChatGPT has demonstrated the ability to understand and generate coherent and contextually relevant
responses across a wide range of topics. This article provides an in-depth examination of the
architecture, training methodology, and applications of ChatGPT in the field of artificial intelligence,
shedding light on its strengths, limitations, and potential implications for various industries. It was
created by the artificial intelligence research firm Open AI. ChatGPT is a natural language
processing (NLP) model that combines GPT-2, a transformer-based language model developed by
Open AI, with supervised and reinforcement learning techniques to fine-tune it (an approach to
transfer learning) on the GPT-3 group of large language patterns developed by Open AI. The model
enables users to interact naturally with an AI system through text-based conversations. It could be
used for customer service applications and to create virtual assistants for voice and text
conversations. ChatGPT also provides features such as topic detection, emotion detection, and
sentiment analysis capabilities to help users understand their conversation partner better.
Additionally, it has the capability to generate multiple conversation threads in order to create more
realistic interactions between user and bot. We will also explore some of the challenges facing AI
development and how we can overcome them. This article is about the recent developments in the
field of artificial intelligence (AI). AI has advanced significantly over recent years, with a wide range
of applications and new technologies being developed. And discuss a few of these advancements and
use to improve human lives. In this paper, we discuss how ChatGPT - a Natural Language
Generation (NLG) model powered by OpenAI's GPT-3 technology - can enhance e-commerce via
chat, as well as other sectors such as education, entertainment, finance, health, news and
productivity. We will analyze the current use-cases of ChatGPT in these sectors and explore possible
future applications. We will also discuss how this technology can use to create more personalized
content for users. Finally, we will look at how ChatGPT can help to make customer service more
efficient and effective for businesses.
Keywords: ChatGPT, Natural language processing (NLP), OpenAI, GPT-2, GPT-3, Machine
Learning (ML).

ABSTRACT
Agriculture is first and foremost factor which is important for survival. Machine learning (ML) could
be a crucial perspective for acquiring real-world and operative solution for crop yield issue.
Considering the present system including manual counting, climate smart pest management and
satellite imagery, the result obtained arent really accurate. This paper focuses mainly on predicting
the yield of the crop by applying various machine learning techniques. The classifier models used
here include Logistic Regression, Naà ̄ve Bayes and Random Forest, out of which the Random
Forest provides maximum accuracy. The prediction made by machine learning algorithms will help
the farmers to come to a decision which crop to grow to induce the most yield by considering factors
like temperature, rainfall, area, etc. This bridges the gap between technology and agriculture sector.
Predicting crop yield based on the environmental, soil, water and crop parameters has been a
potential research topic. Deep-learning-based models are broadly used to extract significant crop
features for prediction. Though these methods could resolve the yield prediction problem there exist
the following inadequacies: Unable to create a direct non- linear or linear mapping between the raw
data and crop yield values; and the performance of those models highly relies on the quality of the
extracted features.
KEYWORDS Crop_yield_prediction, Logistic_regression, Naà ̄ve bayes,Random forest,
Weather_api, Climate change, deep learning, Precision algorithm

Abstract
Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.
Keywords: applications; intelligent transportation systems; sensors, vehicle.

Abstract: The ever-growing sophistication and volume of cyber threats necessitate the evolution
of defensive strategies. Traditional signature-based detection methods are increasingly
insufficient against novel and adaptive attacks. Artificial Intelligence (AI) and Machine Learning
(ML) have emerged as transformative technologies, offering powerful capabilities to enhance
cybersecurity postures across various domains. This paper explores the core applications of
AI/ML in cybersecurity, detailing their underlying methodologies and highlighting their pivotal
role in modern threat detection, prevention, and response. We discuss applications ranging from
anomaly detection and malware analysis to user behavior analytics and automated incident
response, emphasizing the advantages and challenges associated with their implementation.

The process of extracting information to identify patterns, trends, and useful data
that would allow the business to take the data-driven decision from huge sets of data is called
Data Mining. Data Mining is the process of investigating hidden patterns of information to
various perspectives for categorization into useful data, which is collected and assembled in
particular areas such as data warehouses, efficient analysis, data mining algorithm, helping
decision making and other data requirement to eventually cost-cutting and generating revenue.
Data mining is the act of automatically searching for large stores of information to find trends
and patterns that go beyond simple analysis procedures. Data mining utilizes complex
mathematical algorithms for data segments and evaluates the probability of future events. Data
Mining is also called Knowledge Discovery of Data (KDD).This process includes various types
of services such as text mining, web mining, audio and video mining, pictorial data mining, and
social media mining. The biggest challenge is to analyze the data to extract important
information that can be used to solve a problem or for company development. There are many
powerful instruments and techniques available to mine data and find better insight from it.
Keywords
ï‚· Data mining
ï‚· Data warehouse
ï‚· Pattern
ï‚· Data cleaning
ï‚· Knowledge extraction
ï‚· Transformation
ï‚· Evaluation

The purpose of this paper is to analyze the women empowerment and technology, as the
technology has changed the way people a business functions, peoples thinking, behaving,
communicating and working at different work places. Technology has been linked to changes
in the world particularly seen as game changer for women who are facing large barriers in
accessing, using and owning technology. While new technologies offer huge potential to
increase economic opportunities-from mobile computer and digital technologies which
facilitate trade and access to local and global markets, to grow in Hi-tech industries which
facilitate and create skill jobs. So, the question arises, does technology really has a potential to
support women’s empowerment? And secondly, what is its impact on their working lives? So,
for as empowerment of the women is concerned, it become a legal these days to provide
protection at working sites apart from other essential facilities. No doubt the such measures are
nominal and cosmetic. The organized sector is governed by legislations such as minimum
wages act, labour welfare regularization act, and contract labour (Regularization and abolition)
act and the factories act. Now, in this post-modern era, technology as an important tool plays
an important role in the development of women and provides opportunities for their
empowerment. ICTs have the potential to exchange information and empower the marginalized
sectors in the society This paper tries to find out the factors affecting ways and means in
participation of women in technology and addresses the issues regarding women empowerment
and technology.
Keywords: Empowerment, ICT, Technology, women

Natural Language Processing (NLP) is the part of Artificial Intelligence that helps computers communicate with humans in their own language and scales other language-related tasks. NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important. Combined with machine learning algorithms, NLP generates systems that learn to perform tasks on their own and get better through experience. NLP-powered tools can help you to classify social media posts by sentiment, or extract named entities from business emails, among many other things. The role of machine learning in the NLP is clearly defined in this paper with its application areas.

This chapter analyses how these women, once they have entered
ICT, find ways of empowering themselves in a field that they
initially experienced as not very welcoming to women, asking how
they succeed in establishing their own sense of belonging in the
field of ICT. The analysis explores how women negotiate to
perceive themselves as fitting into the male- dominated field of
ICT. In this process they mainly have to rely on their own efforts –
their self-empowerment, employing strategies and practices for
making women visible as they strive to identify ICT as a field
where women, too, belong.

The paper concludes by forecasting the future
trajectory of AI/ML in cybersecurity, emphasizing their indispensable contribution to
building resilient and future-proof digital defenses.

Quantum computing represents a revolutionary paradigm shift in computation, harnessing the
principles of quantum mechanics to solve problems intractable for even the most powerful
classical supercomputers. This paper delves into the fundamental concepts that underpin this
technology, exploring key topics such as qubits, superposition, entanglement, and quantum
gates. It discusses powerful quantum algorithms like Shor's and Grover's, examines the
significant challenges of decoherence and error correction, and highlights diverse hardware
implementations currently under development. Finally, it explores the broad range of
transformative applications across various sectors and provides an outlook on the current
state and future trajectory of this rapidly evolving field.

The escalating sophistication and volume of cyber threats present an unprecedented challenge
to traditional cybersecurity paradigms. As adversaries leverage advanced techniques, the need
for a proactive, adaptive, and intelligent defense mechanism becomes paramount. This paper
explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in
modern cybersecurity. We delve into the fundamental principles of AI and ML, outlining
their core functionalities and how they are applied to detect, prevent, and respond to
cyberattacks.

The integration of predictive analytics with social media presents a unique opportunity for
organizations to leverage real-time data for actionable insights. By combining the vast amount of
unstructured data generated on social media platforms with advanced analytics techniques,
businesses can predict future trends, consumer behavior, sentiment shifts, and emerging patterns.
Predictive analytics can enhance decision-making, improve marketing strategies, and optimize
customer engagement.

The integration of predictive analytics with social media presents a unique opportunity for
organizations to leverage real-time data for actionable insights. By combining the vast amount of
unstructured data generated on social media platforms with advanced analytics techniques,
businesses can predict future trends, consumer behavior, sentiment shifts, and emerging patterns.
Predictive analytics can enhance decision-making, improve marketing strategies, and optimize
customer engagement. This paper explores the process of integrating predictive analytics with
social media data, highlights various use cases, and discusses the benefits, challenges, and tools
associated with this integration. Ultimately, the fusion of these two technologies offers
businesses a powerful means to stay ahead of competition and tailor their strategies to ever-
evolving customer needs.

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.

Conventional optimization techniques have difficulties capturing the details of complex problems while quantum computing, despite its potential, is limited by available hardware resources. This work studies hybrid quantum-classical algorithms with focus on the integration of both paradigms. Therefore, quantum inspired optimization techniques are introduced, for example QPSO, QGA, and QACO, which enhance exploration, parallel processing, and convergence for the classical methods. Novel quantum-enhanced operator and its multi-objective extensions with new evaluation metrics are the focus of this work. Their engineering application, influence in the machine learning domain, and in- dustry uses reflects the importance of these developments. In addition, in detail out- lined problems, gaps in the research, and unresolved questions are described, and with them the outlook of quantum inspired approaches for optimization in computation is emphasized.
Keywords: Quantum-inspired optimization, Hybrid quantum-classical algorithms, Quantum computing, Traditional optimization techniques, Quantum-inspired algorithms (QPSO, QGA, QACO)

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.

The adoption of the IIOT as a complementary technology to operational technology has enabled a new level of standardize data access and process visibility. This convergence of IT,OT and IIOT also created new cyber security vulnerabilities and risk that must me managed

AI is crucial for modern bonnet detection,moving beyond signatures to analyze network traffic patterns.

The continuous rise of cyber threats has exposed the limitations of conventional,
signature-based defense mechanisms. Such methods often fail to recognize sophisticated
and previously unseen attacks. Artificial Intelligence (AI) and Machine Learning (ML) are
emerging as vital components in strengthening cybersecurity by enabling intelligent
threat detection and rapid incident response. This chapter examines how AI and ML
contribute to anomaly detection, user behavior analysis, malware classification, and
automated response strategies. It also presents case studies, highlights the advantages of
AI-driven systems, discusses associated challenges, and outlines future directions in
developing proactive cyber defense strategies.

Presently the most appropriate way to express our thoughts and feelings with physically challenged persons (defend dumb) the communication pays the only way. Nevertheless, the number of issues is arising with common people who are not well aware with sign languages making it difficult for the deafand dumb people to withstand in current generation. Here I develop a worthwhile system that helps in providing speech to speechless person with aid of smart glove assistance. Although the market has some data gloves for some renowned gaming and other virtual reality applications, there is no such complete system that is available for the translation of sign language into speech. To attain this, I have used the number of flex sensors fitted with the glove. These sensors respectively then sense the finger movement that measures information arising from physical interaction with its environment. According to the output generated from the flex sensor, ATmega328 will display the corresponding message over LCD in which the Analog signal is converted into the digital signal using ADC converter. The generated text message is then converted into voice using speak jet and this voice is heard with the help of speaker. Hence making

Cyber security has gained significant importance in protecting networks from different
cyber- attacks like Intrusions, Denial-of- Service (DoS), Eavesdropping, Rushing Attack,
etc. In this paper, a Hadoop based framework is proposed to identify the malicious IoT
traffic using optimal Deep learning method. The novelty of this paper is to utilize a big data
platform for benchmark IoT datasets to minimize computational time. The IoT benchmark
datasets are loaded in the Hadoop Distributed File System (HDFS) environment. The
proposed model will be compared with the existing models to show the system
effectiveness.

Machine Learning (ML) personalizes online learning by analyzing student data to create adaptive paths, offering tailored content, real-time feedback, and predictive support, moving beyond one-size-fits-all models to boost engagement and outcomes through customized difficulty, relevant resources

It refers to systems and machines that can perform tasks.

Human computer Interaction is a multidisciplinary field studying how people use and design interactive computing systems,focusing on creating user-friendly interfaces to improve user experience.

AI and human expertise form a powerful cybersecurity duo: AI handles massive data analysis, rapid threat detection, and automation of repetitive tasks (like alert triage), while humans provide critical thinking, contextual understanding, ethical judgment, and creativity to address novel threats, leading to resilient, proactive defenses that leverage AI's speed with human oversight for a truly effective, adaptive security posture.

The insurance industry has long been plagued by fraudulent activities, resulting in substantial financial lossesand operational inefficiencies. To mitigate this challenge, the integration of data science techniques has emergedas a promising approach in detecting and preventing fraudulent insurance claims. This study investigates theapplication of data science methodologies in fraud detection within the realm of insurance claims. The research
begins by elucidating the prevalence and detrimental impacts of insurance fraud on both insurers andpolicyholders, emphasizing the urgency for effective detection mechanisms. Subsequently, it delineates thefoundational principles of data science and its relevance in the context of fraud detection. Key data sciencetechniques such as machine learning algorithms, anomaly detection, and predictive modeling are explored fortheir applicability in identifying fraudulent patterns and behaviors within insurance claims datasets. Moreover,the study delves into the challenges and limitations associated with implementing data science solutions in theinsurance sector, including data quality issues, privacy concerns, and interpretability of models. Strategies toaddress these challenges are proposed, encompassing data preprocessing techniques, feature engineeringmethodologies, and model explainability frameworks.

Blockchain Technology is the emerging component of IT and Computing and is a kind of
encrypted record of data works on distributed database. This is furthermore dealt with the data
related to the transaction, contract, independent record, etc. Digital ledger is important and also
applicable in various platforms, not bounded in a particular place; and required in financial
activitiesandservicesandhenceitisworthyinhealthydigitalcurrencyandtransactions.Financial
transaction is not associated with the third party and it keeps the data encryption and here
participantsnoneedtosharepersonaldataandthusBlockchainTechnologyisassociatedwiththe data
breach. Insignificantly blockchain not only reduces the data breach but also supports the multiple
numbers of shared copies and it works on the same database. It helps in wage a data breach
attack. Earlier it was known as Blockchain but gradually it is treated as a Technology and
considered as important in Information Technology and impacting the financial sector rapidly.
Blockchain Technology is required in various tangible and intangible asset management and
required in tracking including recorded effectively within a network and ledger. Blockchain is
thereforeisatool,atechnique,aswellasaprocedureforsophisticatedfinancialmanagement.This paper
is based on existing literature emphasizing Blockchain Technology with features, characteristics
with a focus on its types.

The technology innovation has been changing every day. However, that does not
necessarily live upto what we expect. The maximization of the technology innovation value,
esp. in the development of front-end business, needs the guarantee from technology
management. And the optimization of the strategies in managing the development of front-
end business can be helpful in making an overall plan and coordinating successfully among
different posts in the department. The technology management can improve the efficiency in
the development of front-end business. Optimization technology changes the design process
into a process driven directly by computational analysis. Different techniques are discussed
and classified with respect to their applicability in design. Gaspin transferring technology to
practical application as well as missing solutions are identified.

Artificial Intelligence (AI) is fundamentally transforming the landscape of cybersecurity by
introducing innovative approaches to threat detection, response, and prevention. Traditional
cybersecurity methods, often reliant on static rules and manual interventions, are increasingly
inadequate in addressing the complexity and volume of modern cyber threats. AI-powered
systems leverage machine learning, natural language processing, and behavioral analytics to
proactively identify vulnerabilities, detect anomalous activities, and respond to attacks in real-
time with unprecedented speed and accuracy. This paradigm shift not only enhances the
efficiency of cybersecurity operations but also enables adaptive defense mechanisms that evolve
alongside emerging threats. However, the integration of AI in cybersecurity also presents new
challenges, including the potential for adversarial attacks against AI models and ethical concerns
around automation and privacy. This paper explores how AI is rewriting the rules of
cybersecurity by reshaping threat intelligence, automating incident response, and fostering a
more resilient cyber defense ecosystem.