In an era of rapidly increasing technology, social media plays a crucial role which allows people to interact with each other, sharing knowledge, and shaping influence. Identifying the most important influencers within complex networks is essential for effectively spreading information globally. These influencers are pivotal in various domains, such as marketing, information dissemination, and opinion formation. Various centrality measures like isolating centrality, local-global centrality, closeness, betweenness, and degree centrality etc., have been developed to identify influential nodes. These measures are categorized into two types namely: local and global measures. The local measures rely solely on local information, resulting in lower accuracy, whereas global metrics uses global information, which increases computational complexity. To tackle these issues, we propose a novel Mixed Centrality(MC) metric, based on the local average shortest path along with semi local and isolating centrality. To calculate the efficiency of MC, we use SIR model used for calculating the effect of data dissemination. Subsequently, we use Kendall Tau’s coefficient for calculating the similarity between our method and existing centrality measures on real-world datasets, such as bio-celegans and fb-pages-politicians

The emerging disciplines of machine learning and deep learning have brought about an innovative period, restructuring domains such as image identification, natural language comprehension, and autonomous systems. However, there are still complex optimisation and decision-making difficulties that need to be addressed. Swarm algorithms, inspired by the coordinated movement of natural swarms, are emerging as innovative solutions in this field. This study investigates the productive convergence of machine learning, deep learning, and swarm algorithms, with the goal of unleashing their combined potential. We explore the core principles and mechanisms of swarm algorithms such as particle swarm optimisation, ant colony optimisation, bee-inspired algorithms, firefly algorithms, and bat algorithms. These decentralised algorithms, inspired by nature, are highly effective in addressing various optimisation goals by imitating the self-organizing behaviours observed in natural systems. Additionally, we explore the complex incorporation of swarm algorithms into the operations of machine learning. We aim to utilise swarm-based techniques to enhance the performance and generalizability of machine learning models by optimising hyperparameter tweaking, model selection, and feature engineering. This chapter examines the use of swarm intelligence in the emerging subject of neural architecture search, which is crucial for automating the complex process of designing deep neural networks. In order to strengthen our comprehension, we undertake a journey through captivating instances and tangible implementations in fields like robotics and healthcare. This emerging discipline combines the collective intelligence of groups of organisms with the data-driven capabilities of machine learning to create innovative solutions that can improve decision-making, optimise intricate systems, and drive progress in the field of artificial intelligence

Swarm intelligence plays a crucial role in enhancing the performance of IoT and edge computing. Swarm intelligence, a natural systems-inspired collective decision-making paradigm, has helped to solve most of the existing issues like channel selection, routing table optimization, and scheduling operations in IoT networks. This study discusses how swarm intelligence might improve anomaly detection, energy-efficient routing, and scalable, decentralized algorithms. IoT, edge computing, and swarm intelligence enable efficient data processing, network performance, and novel solutions to complicated issues. Swarm intelligence enhances IoT and edge computing systems, bringing new ideas and solutions for the growing environment of interconnected devices

In complex networks, identifying influential nodes becomes critical as these networks emerge rapidly. Extensive studies have been carried out on intricate networks to comprehend diverse real-world networks, including transportation networks, facebook networks, animal social networks, etc. Centrality measures like degree, betweenness, closeness, and clustering centralities are used to find influential nodes, but these measures have limitations in implementation with large-scale networks. These centrality measures are classified into global and local centralities. Semi-local structures perform well compared to local and global centralities but efficient centrality for finding influential nodes remains a challenging issue in large-scale networks. To address this challenge, a Semi-Local Average Isolating Centrality (SAIC) metric is proposed that integrates semi-local and local information to identify important nodes in large networks, along with the relative change in average shortest path. Here, we consider extended neighborhood concept for selecting the nodes nearest neighbors along with the weighted edge policy to find the best influential nodes by using SAIC. Along with these, SAIC also consider isolated nodes which significantly impact the network connectedness by maximizing the number of connected components upon removal. As a result SAIC differentiates itself from other centrality metrics by employing a distributed approach to define semi-local structure and utilizing an efficient edge weighting policy. The analysis of SAIC has been performed on multiple real-time datasets using Kendall tau’s coefficient. Using the Susceptible-Infected-Recovered (SIR) and Independent Cascade(IC) models, the performance of SAIC has been examined to determine maximum information spread in comparison to the most recent metrics in some real-world datasets. Our proposed method SAIC performs better in terms of information spreading when compare with other exisiting methods, with an improvement ranging from 4.11% to 17.9%

In today’s rapidly evolving digital landscape, blockchain and artificial intelligence (AI) are at the forefront of transforming various industries, and education is no exception. The convergence of these two revolutionary technologies promises to reshape the modern education system by enhancing data security, promoting personalized learning, and creating decentralized frameworks for record-keeping and credentialing. This book delves into how blockchain and AI can drive a more inclusive, efficient, and secure educational ecosystem, where student-centered approaches and data integrity are paramount.This book is organized into several sections, each exploring the distinct roles of blockchain and AI within education. It begins with an introduction to the fundamental principles of these technologies and an overview of their individual strengths. Following this, chapters examine blockchain’s role in secure credential verification, decentralized learning platforms, and the protection of digital records. Next, the discussion shifts to AI applications, covering adaptive learning models, predictive analytics, and AI-driven administrative support. Finally, the book provides real-world case studies and future projections on how blockchain and AI together can tackle challenges in education, such as data privacy, resource distribution, and student engagement, ultimately creating an interconnected and resilient educational framework.This book is designed for educators, administrators, policymakers, technology enthusiasts, and researchers who are interested in the transformative potential of emerging technologies in education. It serves as a comprehensive guide for those looking to understand the practical applications and implications of blockchain and AI in the modern education system.

The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called 'AI-based Research Companion (ARC)' has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.

This paper presents the System for Emotion and Engagement Recognition in Education (SEERE), a cutting-edge advancement integrating computer vision and deep learning tech-nologies to evaluate real-time student engagement through facial emotion recognition and eye tracking. SEERE, a transformative educational tool built on the robust YOLO V8 architecture, customizes the FER2013 dataset, making use of meticulously annotated emotion and eye position data. It goes further, es-tablishing a unique ‘concentration metric,’ a quantitative index of student engagement, bridging a gap in modern responsive teaching approaches. Higher concentration metrics signal height-ened student engagement, offering educators real-time data to adjust teaching techniques and feedback accordingly. The paper provides a thorough review of facial emotion recognition models, setting the stage for understanding the innovative strides made by SEERE. Detailed discussions on the prototype's design and architecture are followed by initial experimental results, reinforcing the system's validity and potential

Drones, also known as Unmanned Aerial vehicles(UAVs), are increasingly used in various applications, including agriculture, construction and infrastructure, environmental conservation, water resources management(WRM) etc. Multiple drones are interconnected with each other as UAV swarms. These UAV swarms offer a versatile and cost-effective tool for WRM. This paper proposes the multi-drone network for WRM known as HydroDrone. The purpose of HydroDrone is to provide valuable data and insights to support decision making and improve the resilience of water supply systems on the verge of ever-changing environmental conditions. In addition, we discussed task allocation to individual drones within the swarm. HydroDrone schedules and balances the task load for water resource management. We also introduced security to multi-drone communication with the base and core stations by incorporating blockchain technology due to its decentralized nature.

The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience. © 2024 IEEE.

The significance of student affective states in the learning process has been widely acknowledged. These emotional states experienced by students play a crucial role in shaping their engagement, motivation, and overall learning experience. Simultaneously, studies in learning analytics have showcased the potential of leveraging the abundant data gathered by e-learning systems such as ITS to identify patterns in student behavior for highlighting the indicators of student learning outcomes. In our current research investigation, we have examined four crucial affective states, namely Boredom, Frustration, Confusion, and Concentration, along with student interaction data collected from the Assistments platform comprising 82 features. Our research has a dual purpose. The first objective is to gain insights into the influence of student affective states and scaffolding on learning progress within a blended learning environment. The second objective is to develop a transparent, data-driven framework based on machine learning that improves educational outcomes. We utilized clickstream and knowledge component data to employ three machine learning models, specifically Random Forest, Linear Regression, and neural network, for the prediction of student performance. We compared these models both in the traditional approach and our proposed framework, MLF-DSM. Furthermore, we employed XAI techniques, specifically SHAP, to investigate and enhance the transparency of the results obtained from this black box model. Overall, our study highlights the applicability of MLF-DSM as a dynamic assessment and personalization tool within a blended learning environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Despite the significant advancements in modern medicine, the prevalence rate of epilepsy remains high, affecting approximately 50 million people globally. When epilepsy is identified early, its management can significantly reduce the risk of long-term damage and promote improved living standards. Non-invasive techniques such as electroencephalography (EEG) are routinely used to diagnose this neurological condition by monitoring electrical activities in the brain. However, EEG data’s usage for research and diagnosis purposes creates numerous challenges related to patient privacy and safety measures of such sensitive digital information. This paper introduces a blockchain-enhanced platform fused with machine learning (ML) models that upholds confidentiality when analyzing EEG data as an ideal way to safeguard patient rights and enhance early epilepsy detection procedures. The study proposes a novel solution to secure private EEG data sharing using blockchain technology, discrete wavelet transform (DWT), and ML models that enhance the accuracy and reliability of early epilepsy diagnosis. When creating an ML model for analyzing EEG data related to epilepsy, privacy is one of the top priorities. To prevent unauthorized access, we turned to a blockchain solution that allowed us to secure both the storage and sharing mechanisms used when dealing with these sensitive patient files. This cutting-edge technology was designed with smart contracts, giving patients complete control over who would have permission to view their medical histories without compromising security and confidentiality. After evaluating actual EEG datasets available in real-world settings, it became clear that utilizing our blockchain-enabled approach yielded superior results compared to more traditional methodologies. This proposed approach consists of InterPlanetary File System (IPFS) and smart contracts that give complete control over permissions to access medical records without compromising data security. As per our research findings, blockchain technology has substantial potential to address privacy and security concerns in EEG data study and sharing for epilepsy diagnosis. Our work contributes to the growing literature on the intersection of ML, blockchain and healthcare. It has significant implications for developing more secure and privacy-preserving medical data-sharing systems in futuristic medical care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as 'School Teachers' Electronics Practicals Upskilling Program (STEP-UP),' it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.

This book reviews research works in recent trends in blockchain, AI, and Digital Twin based IoT data analytics approaches for providing the privacy and security solutions for Fog-enabled IoT networks. Due to the large number of deployments of IoT devices, an IoT is the main source of data and a very high volume of sensing data is generated by IoT systems such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT systems is a fundamental research issue. For the deployment of the Fog-enabled-IoT system in different applications such as healthcare systems, smart cities and smart grid systems, security, and privacy of big IoT data and IoT networks are key issues. The current centralized IoT architecture is heavily restricted with various challenges such as single points of failure, data privacy, security, robustness, etc. This book emphasizes and facilitates a greater understanding of various security and privacy approaches using the advances in Digital Twin and Blockchain for data analysis using machine/deep learning, federated learning, edge computing and the countermeasures to overcome these vulnerabilities.

The profound growth in the human population over the past two centuries has created a new issue in food security. Due to the high demand for food, there is an increasing burden on the agricultural supply chain (ASC) to satisfy the hunger of every individual. As a result, there is a possibility for spoilt or contaminated food to enter the ASC. If the end-consumer consumes these bad food products, it can lead to food poisoning and even death in certain circumstances. In order to ensure that the food delivered to the consumer is safe, it is necessary to monitor the food product as it passes through the different entities present in the ASC. The traditional ASC lacks traceability and reliability. Traceability is necessary in determining the origin of a crop, while reliability is necessary in preventing foul play by any entity. Therefore, developing a traceable and reliable system for the existing ASC model has become very important. The transparent, decentralized, and immutable qualities of Blockchain, along with the help from IoT devices, will allow us to actively trace the food from farm-to-fork as it passes through the supply chain while maintaining a high reliability between each entity. Thus, this paper proposes a novel ASC model, incorporated using Blockchain and IoT technology, to mitigate the traceability and reliability issues in the ASC.

The proliferation of agricultural supply chain encompasses participants such as farmers, intermediate silos, transformation plants, and clients. Managing this agro-supply chain involves various functions related to the flow of both materials and information. Before entering the market, crop protection products and inputs undergo rigorous testing and regulatory scrutiny. Despite these measures, counterfeit products reach end-users due to insufficient transparency and the sharing of outdated information among stakeholders. To address this issue, the paper suggests a three-tiered integrated solution: the Product layer, Blockchain layer, and SSI layer. This strategy involves attaching Near-Field Communication (NFC) tags to the packages at the product layer, with the blockchain monitoring each step of the supply chain. The NFC tags can be read to verify the authenticity and other details of the product. Certifications for products, inputs, and the identities of dealers and consumers are stored as self-sovereign IDs in digital wallets. The authenticity details of producers undergo auditing by the certification authority, which is then transferred to the verification authority. The Verifier confirms these details and generates a verifiable presentation received by the consumer, enabling them to make informed purchases. This approach eliminates product tampering and the involvement of unverified producers and dealers in the supply chain. The comprehensive explanation and investigation of proposed framework state adequate guidelines to make counterfeit resistance agricultural supply chain system.

The rampant adoption of digital technologies made momentous changes in all economic sectors. The agriculture sector cannot abstain from the digital revolution. Agriculture and farming are one of the oldest and most important professions in India. The sector remains the backbone of the Indian rural economy, which desperately demands technological impetus for the socio-economic development of rural areas. Smart agriculture is a revolution in the agriculture industry which helps to guide the actions that are required to modify and reorient the agricultural systems. The paper made an extensive survey of various technologies proposed for the agriculture sector. We have surveyed various smart agricultural applications developed and proposed a taxonomy for classifying them. Network infrastructure and connectivity remains the major challenge for rural areas. The paper explores the viability of deploying IoT-based technologies in agricultural sectors along machine learning techniques to optimize resource utilization, planning and cultivation, marketing, pesticide selection, price prediction, etc. An in-depth coverage of recent research works is also mentioned which will help the future researchers to address specific challenge and adopt suitable technology to help the farmers to improve their productivity and better decision making in cultivation. Apart from listing the applications, we also propose an architecture for smart agriculture and implemented smart price prediction model for crops like cotton and cardamom along with a prototype for smart irrigation.

The era of digital information has witnessed the alarming surge of disinformation, posing a serious challenge to the reliability of information and societal cohesion. In response, the need for robust and efficient methods to identify false information has become paramount. This paper introduces an innovative Hybrid Deep Learning approach that harnesses the capabilities of deep neural networks to improve the precision and dependability of information analysis within information systems. This approach combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with a particular emphasis on the examination of textual and multimedia content. By integrating both CNNs and RNNs, this method adeptly captures spatial and temporal features, enabling the evaluation of textual and visual content from diverse sources. Moreover, the approach incorporates attention mechanisms to assess the relevance of different elements within the content, facilitating the fine-grained differentiation between authentic and deceptive information. A comparative examination of several methodologies has been carried out. The results exhibit a substantial enhancement in the precision of disinformation identification when compared to conventional machine learning methods and standalone deep learning models.

Cognitive Radio Ad hoc Networks (CRAHNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users to opportunistically access underutilized spectrum bands. However, efficient and dynamic spectrum access in CRAHNs remains a challenging task due to the dynamic nature of the network and the unpredictable spectrum availability. This paper proposes an AI-based scheduling protocol for Cognitive Radio Ad hoc Networks (AI-SCAN) to address the spectrum access problem in CRAHNs. The protocol utilizes machine learning techniques to enable intelligent decision-making for spectrum allocation and scheduling. Simulations are conducted to evaluate the performance of AI-SCAN in comparison to existing scheduling protocols. The results demonstrate that AI-SCAN achieves superior performance in terms of spectrum utilization, network throughput, and fairness among secondary users. The protocol effectively balances the trade-off between maximizing spectrum utilization and minimizing interference, thereby enhancing the overall efficiency and reliability of CRAHNs.

Market deregulation of agricultural commodities requires in-depth analysis of day-to-day transactions. The analysis helps to draw greater insights into the market dynamics and builds better policies and advisory system for the farming sector. Reformation to the agricultural commodity market impel to deregulation of market transactions and increasing digitalization of transactions for better transparency across markets. These government policies have been taken based on the need of increasing farmer’s income from farming activities. These policy decisions give farmers immense opportunities for better realization of their farm outputs. To achieve the objective, there is an inevitable need for analyzing the market dynamics to draw better insights from the transactions. Also, farmers face massive loss due to these uncertain fluctuations in the price of agricultural commodities. Price prediction models can help farmers in making necessary decisions and help them in reducing the loss caused by price fluctuations. In this paper, we have chosen cotton as a commodity in which the price and volume of cotton over seven years from the Adoni market in Kurnool district of Andhra Pradesh, which is one of the largest cotton markets in the country market in Andhra Pradesh. The data consist of daily prices and volume of transactions of 7 years starting from 2011 to 2017. We have used machine learning techniques such as the ARIMA model for predicting the prices of cotton.

The innate ability to recognize facial expressions and associated emotions is fundamental to human communication. Technology advancements have enabled computers to perform similar tasks to a considerable extent, opening versatile applications in diverse domains. In particular, Facial Emotion Recognition (FER) technology has recently been widely explored for investigating student engagement in classroom settings. While previous research studies mainly captivated the FER practice in engagement detection, far too little attention has been paid to the real-time emotional states of students during classroom interactions. In this regard, this paper introduces the In-Class Student Emotion and Engagement Detection System (iSEEDS), a novel AI-based approach for pinpointing learners' emotional states during classroom lectures. The iSEEDS employs Convo-lutional Neural Network (CNN) models for emotion detection and corresponding eye movement analysis. The system can help educators respond in real-time to students' emotional states and engagement levels. It can support responsive teaching by initiating remedial feedback in accordance with students' current emotions and engagement. A detailed literature review of existing emotion recognition models is presented as a background of iSEEDS development. Then the initial prototype model design and illustrative test results are discussed. Potential applications of iSEEDS and future research directions are also elaborated.

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Twitter is an excellent initial point for social media analysis. People directly share their opinions through Twitter with the public. One of the very common analyses which can perform on many tweets is sentiment analysis. The 2020–2021 Indian farmer’s protest is a protest against three farmer acts which were passed in parliament of India in 2020. In this paper, we have performed sentiment analysis of the protest of farmers in India (2020–2021) by considering the opinion of the people. The data is taken from hashtags that are related to farmers protest and some minor hashtags related to farmers. Based on the result of analysis, we conclude the impact of the protest done to repeal the farmer’s act, on India. On analysis, we obtained the result as percentages of positive, negative and neutral.

Language diversity in India is an extension of the vast cultural diversity that exists in Indian society. The globalization and presence of multinational companies made an inevitable need of English language, usage including in academics, though there are regional universities that use local languages as the medium of instruction. The emerging digital content market cannot elude the language diversity prevalent in the Indian population. The paper explores the possibility of finding common patterns among Hindi-speaking university-educated netizens regarding their first language preferences. These common patterns could be used to build a recommendation system for multilingual content-providing systems. The dataset that is used for building the system is collected as part of pilot work for creating a database in line with the Language Experience and Proficiency Questionnaire (LEAP-Q) for Indian languages. The questionnaire is formalized by the Language Technology Research Centre, Hyderabad, in collaboration with Pauranik Neuro Center, Indore. The questionnaire comprises questions related to language preferences and usage in different situations and reading habits. The questionnaire is devised to assess the Hindi language usage among bilingual Hindi native speakers. For this purpose, respondents are university-educated Native Hindi language users. The analysis shows that there is little relation to the language preferences between formal language usage and informal language usage. The reading habits of Hindi newspaper is observed as more of a personal choice, rather depends on any other language preferences. The analysis shows that three to four variables are enough to estimate preferences of the first language reading habit (Hindi). The proposed method could be used to recommend the right language content to bilingual readers at an average accuracy of around 50%.

The COVID-19 pandemic has reformed the teaching-learning processes in engineering education across the globe. Virtual classrooms substituted physical classrooms with the widespread use of online meeting platforms. The proliferation of virtual classrooms not only paved the way for accelerated digital transformation but also brought back some elementary issues in engineering education. Many engineering students face difficulties in comprehending the fundamental concepts in their courses during virtual learning. As real-world engineering solutions depend on conceptual clarity, misconceptions of basic engineering principles need to be taken seriously. If not identified, analysed and corrected with constructive feedback, misconceptions on various engineering topics can create challenging obstacles in learning. Against this backdrop, this research study introduces a novel solution titled Classification of Students Misconceptions in Individualised Learning Environment (C-SMILE). The primary objective of the C-SMILE system is to examine the usefulness of personalised automated feedback to students to enhance their conceptual understanding by pinpointing their misconceptions. Besides, we propose a method by which students' misconceptions can be effectively classified for every instructional objective in every engineering course using machine learning techniques. Our pilot-study results show that the proposed C-SMILE system can precisely classify students' misconceptions in engineering education settings.

Proteins play a very important part in various vital roles such as understanding a disease and are most commonly used in drug formulation. The process of building DNA-binding proteins that is an ideal combination of deliverability, specificity, and activity is not yet fully solved, nevertheless current platforms offer unique advantages, offset by behaviours and properties over each other. In this paper, we propose a method using deep learning which we can predict and pin-point the exact DNA-protein binding site and their conformation due which a particular decease has risen in a person. The proposed method is able to achieve an accuracy of 89.21% compared to some of the existing methods.

When new technologies are created for the welfare of the humans, it also brings many challenges, particularly when it applied to healthcare. Machine learning is one of such new technology which is implemented to solve many of the problems in healthcare. Machine learning techniques have a huge impact on today's day-today activities. Most of the applications are going to be automated using such techniques. Also, we are living in an era of providing better healthcare services to the human using technologies. In this chapter, we performed a study on role of machine learning techniques in healthcare applications and built prediction system for some of the critical diseases like sickle cell anemia, breast cancer, and heart diseases. We have analyzed some of the biomedical data with existing machine learning algorithms and identified some possible directions to the research community.

Long noncoding RNAs (lncRNA) have a vital role in tumor development. Variation in expressions of IncRNAs affect several target genes related to tumor initiation and development. Recent studies in Carcinogenesis have indicated the importance of IncRNA in cancer progression, diagnosis, and treatment. The purpose of our research is to identify the key cancer-related IncRNAs. It is considered a complex task to identify key IncRNAs in cancer with existing cancer data of tumor patients due to the high dimensionality nature of expression profiles. LncRNA expression profiles of 12309 IncRNAs and 2221 patients are gathered from TCGA. A Computational framework is proposed considering 5 cancer types (Bladder, Colon, Cervical, Liver, Head, and Neck) comprising four Machine learning classification models named K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine. An essential component in the framework is to use models along with the state-of-the-art Variance threshold, L1-based, and Tree-based feature selection algorithms for differential analysis. The study resulted in identifying 234 key IncRNAs capable of differentiating 5 cancer types. The capability of identified key IncRNAs is observed by the performance of classification models resulting in the highest 98.2% accuracy by SVM. Furthermore, the correlation analysis of 234 IncRNAs experimentally validated the results.

In the traditional agriculture in India, the farmers are not able to make profit from their crops. We propose a price prediction model using Machine Learning approaches, which helps the farmers to make appropriate decisions well before cultivation and later on selling their farm outputs. We have chosen cardamom as a case study in which the data being collected over four decades' monthly average prices. We propose an ensemble method of multivariate linear regression model and ARIMA model over the clustered average monthly cardamom prices across forty years. The robustness of the proposed model is evaluated against four-decade monthly price movement.

Agriculture is an innovative way of cultivating crops where the resources are optimized to reduce the harmful impacts on environment and to get better crop yields. India has immense opportunities to make use of the potential of Agri-Tech solutions as majority of farmers possess small land holdings. Ecological sustainability is another significant challenge currently needed to be addressed. The paper propose a Smart-Agri architecture based on digital systems with available technologies for incorporating the factors required for better crop yield and to maintain a sustainable ecosystem. The architecture specifically caters to the need of Small Cardamom which is grown in hilly regions and is particularly much delicate to the climate changes. Due to the rampant use of fertilizers and pesticides, lot of degradation has happened to the Cardamom hill reserve zones over past few decades. In the system, data is to be collected from different parts of a farm using mesh connected ZigBee network making a low-power framework, to monitor soil and plant health and analyze data for finding implications to sort out recommendations meeting sustainability and crop goals. The proposed architecture aims to optimize the resources for small farms by assessing the needs of farmlands. This helps to mitigate the adverse effects on nature.

Agriculture is a sector that plays a crucial role in the economies of many countries around the globe, like India where it contributes 16% of the total economy. Weather forecasting is one of the challenges faced by this sector, due to its dynamic and turbulent nature, the statistical methods fail to provide forecasting at an accurate precision. This paper aims to develop an accurate way to predict the temperature forecast using machine learning techniques especially using Long Short Term memory networks (LSTM). Despite the advances made, there are still significant obstacles to overcome in expanding the use of weather forecasts in the agricultural sector due to the dynamics in climate changes. These include the need for improved model accuracy, quantitative evidence of the utility of climate predictions as instruments for agricultural risk management and addressing major chances of disease incidence which are usually seasonal and depends on parameters like temperature and rainfall. The goal of this study is to forecast parameters that could help farmers to make an informed decision so as to reduce the losses by taking required proactive measures. This paper provides a detailed analysis of weather forecasting techniques and explores future research goals in this field.

In today's life, emotional wellbeing is an important aspect to be analyzed particularly for school going children. If not properly analyzed, the emotional difficulties may hinder academic, personal and social growth, resulting in a lifetime of difficulties for such individuals. Only when a child is emotionally secure and content, he/she gives their best to every challenge they face. Schools should create a progressive learning environment where academic achievement is related not only to successful learning strategies, but also to good mental well-being. In this paper, we aim to explore the meaning, situation, and parties associated with happiness in children and to deliver the best tool to detect the state of the children's mental health regarding whether they are happy or sad and based on machine learning techniques.

Internet of Things (IoT) is an emerging technology, which integrates all everyday objects and services into the common network platform like Internet. With the help of IoT, every physical object is converted as smart object. Each of the physical object is identified uniquely and can sense the information in the environment and to take decisions based on that. IoT is useful in many of the applications, including home automation, retail, industry control, agriculture, health monitoring, etc. In healthcare domain, some of the IoT-based applications include, monitoring old people for fall detection, monitoring temperature conditions inside refrigerator for storing medicines, vaccines, and monitoring of patient's condition in hospitals and home. Machine Learning applications in Healthcare also seems to be endless. In this paper, we have investigated IoT based applications in healthcare and performed an experimental analysis of healthcare data-sets using machine learning techniques.

We present a study of the novel mathematical model, CISER, in this work. The proposed model is a more general variant of the SIR epidemic model for representing information spread. The work provides an analysis of the mathematical model, in particular, the inclusion of carrier and exposed states in providing a more generalized representation toward SIR. The model also provides an accurate description of the disease amoebiasis and relates the disease characteristics to message propagation. We derive various properties of importance and depict the model representation of various states of spread (message propagation) through numerical results. Based on this model, we then provide an epidemic controlled flooding mechanism for information spread in a mobile ad hoc network (MANET). This is accomplished through a reactive IPv4-based routing protocol built on top of the MAC 802.11b layer. We further present our simulated results and perform a comparative analysis against other epidemic routing protocols such as SIR and SEIR. We discover our model performing better in terms of most performance metrics.

Internet of Things (IoT) is an emerging paradigm which aims to inter-connect all smart physical devices, so that the devices together can provide smart services to the users. Some of the IoT applications include smart homes, smart cities, smart grids, smart retail, etc. Since IoT systems are built up with heterogeneous hardware and networking technologies, connecting them to the software/application level to extract information from large amounts of data is a complex task. In this paper, we have surveyed various architecture and protocols used in IoT systems and proposed suitable taxonomies for classifying them. We have also discussed the technical challenges, such as security and privacy, interoperability, scalability, and energy efficiency. We have provided an in-depth coverage of recent research works for every mentioned challenge. The objective of this survey is to help future researchers to identify IoT specific challenges and to adopt appropriate technology depending on the application requirements.

Steganography is an active research area in the field of information security. Audio steganography refers to the process of embedding a secret message into an audio signal for secure message transmission. In this paper, we propose a novel secure and robust audio steganography scheme with good embedding rate. The key idea behind the proposed scheme is that a secure encryption scheme is used in the steganography scheme to encrypt the secret message. Further, a random least significant bit plane will be selected by using the genetic algorithm. The encrypted secret message bits will be embedded into the selected bit-plane. The genetic algorithm helps to reduce the distortions on the stego audio after the data hiding process. The selection of higher bit-planes for data hiding process will help to achieve better robustness against noises. The experimental study of the proposed scheme on a variety of audio signals shows that the proposed scheme performs better than the well-known least significant bit steganoaraphy scheme.