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 experts�?? 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
chain�??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.

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.

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.

The top priority of any business is a constant need Increase sales and profitability. one of the causes of a reduction in profit occurs when an existing customer stops trading. When a customer leaves or terminates the company, potential sales or cross-selling opportunities are lost. When the customer leaves the store without any advice. It can be difficult for companies to respond and take corrective action. Ideally, companies should act proactively and identify themselves chances are you will churn before they leave. customer retention strategies have proven to be less expensive than attracting new ones client. Through data available at the POS(POS) system, extract customer transactions, you can analyze their buying behavior. In this paper Features are created through transactional data and how they are created Identified as important for predicting retail churn industry. Data provided in this document refer to local resident’s supermarket. Thus, dropouts are identified and results are obtained. The results obtained are based on real scenarios. The novelty of this paper is the concept of implementing deep learning algorithms. Convolutional Neural Networks and Restricted Boltzmann Machine is the deep learning technique of choice or restricted Boltzmann machine gave the best results 83% in predicting customer attrition.
Keywords: customer attrition, deep learning, retail grocery industry.

Deep learning models are ideal for procure perception into the user experience in customer service reciprocity.
Deep learning can predict the sentiment of the customer during customer service reciprocity which is crucial for
improving the customer adventure. Automated customer service systems are great for reducing phone queues efficiently
solving simple problems but when the customer is stressed with a large problem, most people want to speak to a human
operator. The traditional solution to this would be to invest in a more refined automated service or invest in more human
operators. With deep learning, these costs can be avoided by adeptly allocating the human operators’ time to problems
where human interaction is necessary. Customer service phone calls can also be analysed using speech-to-text technology
to analyze what the customer is saying, in combination with analyzing the audio directly to understand how the customer
is saying it. Customer behavior analysis is an essential issue for retailers, allowing for optimized store performance,
enhanced customer experience, reduced operational costs, and consequently higher profitability. Nevertheless, not much
attention has been given to computer vision approaches to automatically extract relevant information from images that
could be of great value to retailers. In this paper, we present a low-cost deep learning approach to estimate the number
of people in retail stores in real-time and to detect and visualize hot spots. For this purpose, only an inexpensive RGB
camera, such as a surveillance camera, is required. To solve the people counting problem, we employ a supervised
learning approach based on a Convolutional Neural Network (CNN) regression model.
Keywords -Deep learning, CNN, Supervised learning

In this paper, Predictive machine learning algorithms have been discussed. This
algorithm are used for various purposes like data image processing, predictive analytics. The main
advantage of using machine learning is that, fast processing and real time production. Today retail
video analytics has gone beyond the traditional domain of security and loss prevention by providing
retailers insightful business intelligence such as queue enumeration and queue data. Such
information allows for enhanced client adventure, reduced operational costs, and ultimately higher
profitability.
Index Terms - Retail, video surveillance, Machine Learning, business intelligence, computer vision

Deep Learning for Retail Product Conceding: Protest and Strategy

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. Videos have ample amount of information in the form of frames that can be utilized for feature extraction and matching. However, face images in not all of the frames are 'memorable' and useful. Therefore, utilizing all the frames available in a video for recognition does not necessarily improve the performance but significantly increases the computation time . A deep learning algorithm is then proposed that utilizes a stack of denoising auto-encoders and deep Boltzmann machines to perform face recognition using the most memorable frames.

Within the ever-growing and quickly increasing field of cyber security, it is nearly impossible to
quantify or justify the explanations why cyber security has such an outsized impact. Permitting
malicious threats to run any place, at any time or in any context is a long way from being acceptable,
and may cause forceful injury. It particularly applies to the Byzantine web of consumers and using
the net and company information that cyber security groups are finding it hard to shield and contain.
Cyber security may be a necessary thought for people and families alike, also for businesses,
governments, and academic establishments that operate inside the compass of world network or
net. With the facility of Machine Learning, we will advance cyber security landscape. Businesses
these days are gathering immense amounts of user information. Information is at the heartbeat of
any business-critical system you’ll be able to think about. This co-jointly includes infrastructure
systems that are being implemented these days. Today’s high-tech infrastructure, that has network
and cyber security systems, is gathering tremendous amounts of data and analytics on almost all
the key aspects of mission-critical systems. Whereas people still give the key operational oversight
and intelligent insights into today’s infrastructure, machine learning and AI are gaining pace and
gathering immense momentum in most of the areas of today’s systems, whether or not it’s positioned
on premise or within the cyber security house.
Keywords:Machine Learning, cyber security, k-means, Random Forest, SVM etc.

This paper proposes a brand-new shifting item partition set of rules for explicitly
transpose cameras which may be very not unusual place for the out of doors Surveillance device,
the automobile construct-in surveillance device, and the robot navigation device. A -layer based
totally absolutely affine transformation model optimization method is proposed for virtual digital
camera compensation purpose, in which the outer layer era is used to clean out out the non- records
characteristic elements, and the inner layer era is used to estimate a fashionable affine model based
totally absolutely on the RANSAC method. Then the characteristic elements are categorised into
foreground and records in keeping with the detected motion facts. A geodesic primarily based
totally graph reduce set of rules Is then employed to extract the transferring foreground based
totally completely on the labeled functions. Unlike the prevailing worldwide optimization or the
long term characteristic issue tracking based totally completely technique, our set of regulations
only performs on successive frames to phase the transferring foreground, which makes it suitable
for the web video processing packages. The check consequences screen the effectiveness of our
set of regulations in every of the immoderate accuracy and the fast speed.

Cloud computing services are becoming more and more popular. However, the high concentration of data and services on the clouds make them attractive targets for various security attacks, including DoS, data theft, and privacy attacks. Additionally, cloud providers may fail to comply with service level agreement in terms of performance, availability, and security guarantees. Moreover, users may choose to utilize public cloud services from multiple vendors for various reasons including fault tolerance and availability. Therefore, it is importance to have secure and efficient mechanisms that enable users to transparently copy and move their data from one provider to another.

Artificial intelligence (AI) and machine learning (ML) are among the top technology trends in the retail world. They are having a great impact on the industry. Stores are changing. We see it happening before our eyes, even if we don't always realize it. Little by little, they are becoming just one extra step in an increasingly complex customer journey. If you're a retailer searching for new ways to attract customers, analyzing business data is a smart way to begin your search. Data analysis offers insights to help you increase customer engagement, but it's tough to accurately analyze large data volumes on your own. That's where machine learning can come to your rescue. A machine learning model can analyze and break large volumes of complex data into actionable insights, so we can better understand customer behavior and market trends. Using these insights, you can estimate future demand, set competitive prices, personalize offerings for customers, and much more.

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
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.

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
 Data mining
 Data warehouse
 Pattern
 Data cleaning
 Knowledge extraction
 Transformation
 Evaluation

In the current world that is run by technology and network connections, it is crucial to
know what cyber security is and to be able to use it effectively. Systems, important files, data,
and other important virtual things are at risk if there is no security to protect it. Whether it is an
IT firm not, every company has to be protected equally. With the development of the fresh
technology in cyber security, the attackers similarly do not collapse behind. They are consuming
better and enhanced hacking techniques and aim the weak points of many businesses out there.
Cyber security is essential because military, government, financial, medical and corporate
organizations accumulate, practice, and stock unprecedented quantities of data on PCs and other
devices. An important quota of that data can be sensitive information, whether that is financial
data, intellectual property, personal information, or other various kinds of data for which illegal
access or acquaintance could ensure negative concerns.

Digital twin technology is a new technology applied in manufacturing industry. Different types of digital twins may be identified based on the connection and data stream between the physical object and the digital twin. This research focuses on the description of the main features of this new technology and on the newly developed applications due to corporate digital transformation process in the manufacturing industry. Digital transformation in corporate world open up new possibilities for application options of digital twin technology. External data sources such as social media may also be applied for designing and making digital twins in the process of corporate digital transformation process. The development of corporate digital transformation such as artificial intelligence solutions have also opened up new development options. The research elaborated these new ways.

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.

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.