bluuebunny/crossref_metadata_2025 huggingface dataset:
IoT Resources and Their Practical Application, A Comprehensive Study
Authors: Kumar Saurabh Et al.
Published: December 2023

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.
An Android-based Image Steganography Approach to Data Communication Security using LSB and Password-based Encryption
Authors: Esther Hannah M Et al.
Published: December 2023

The study's ultimate goal is to develop an Android app dedicated to Image Steganography, a technique for concealing sensitive information behind seemingly innocuous photographs. This information hiding technique is essential because of its many potential uses. Symmetric encryption, asymmetric encryption, and steganography are all brought together in this method. The initial picture is encrypted using a symmetric technique in this procedure. After that, the encrypted picture is quietly placed using a least significant bits Steganographic approach to conceal the secret key that was encrypted using an asymmetric algorithm. The steganography algorithm's simple but effective security mechanism is a major benefit. Integrating a secret message into a seemingly benign source makes it very difficult to detect the hidden message without prior knowledge of its existence and the appropriate decryption algorithm. The privacy of the concealed data is protected by this feature. The planned Android app would provide a user-friendly interface for using picture steganography methods, making it simple for anybody to choose commonplace photos and secret messages for obfuscation. Both the original picture and the concealed message will be kept secret thanks to the app's robust encryption methods. The hidden message will be seamlessly included into the regular picture using advanced steganographic methods like the least significant bits approach. The creation of this software is motivated by a desire to provide an easily accessible solution for people who want safe communication through hidden messages or encrypted photos. The robust encryption and steganographic technologies, paired with the straightforward interface, will enable users to effectively protect sensitive information. This work will strengthen the area of data security and highlight the need of sophisticated encryption and steganography in modern digital communication.
Enhancing Network Security Through Blockchain Technology: Challenges And Opportunities
Authors: Salwa Shakir Mahmood Et al.
Published: December 2023

The rapid proliferation of digital technologies has ushered in an era where network security is of paramount importance. Traditional security mechanisms have proven insufficient in protecting sensitive data and critical infrastructure from an ever-evolving landscape of cyber threats. Blockchain technology, originally designed to underpin cryptocurrencies like Bitcoin, has emerged as a promising solution for enhancing network security. Blockchain's core principles of decentralization, immutability, and transparency offer a unique approach to addressing vulnerabilities and mitigating risks in the digital realm. This paper examines how blockchain can enhance data integrity, authentication, and authorization processes, thereby fortifying the security posture of networks and systems.

The paper discusses real-world applications and case studies where blockchain has been successfully implemented to bolster network security, such as supply chain management, identity verification, and secure communication protocols. These examples highlight the tangible benefits and opportunities that blockchain presents for organizations seeking to safeguard their digital assets and operations. In conclusion, this abstract underscore the pivotal role that blockchain technology can play in enhancing network security. By addressing challenges head-on and capitalizing on the opportunities it offers, organizations can build resilient, transparent, and secure digital ecosystems that protect against an ever-increasing array of cyber threats. The exploration of blockchain's potential in this context is critical for shaping the future of network security in our interconnected world.
Network Intrusion Detection Using Autoencode Neural Network
Authors: Zakiya Manzoor Khan Et al.
Published: December 2023

In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy.
A Study on Data Protection in Cloud Environment
Authors: S Arvind Et al.
Published: December 2023

Data protection in the online environment pertains to the safeguarding of sensitive or important data kept, analyzed, or sent in cloud-based systems. It entails assuring data confidentiality, integrity, and availability, as well as adhering to appropriate data protection requirements. In a nutshell, cloud data protection seeks to protect data against unauthorized access, deletion, or breaches while retaining its accuracy and accessible to authorized users. This is accomplished in the cloud environment using various security measures, encryption approaches, access controls, disaster recovery and backup processes, and constant monitoring and threat detection.  The research significance of data protection in the cloud environment can be summarized as follows: Security and Privacy: Research in data protection in the cloud helps address the security and privacy concerns associated with storing and processing sensitive data in cloud-based systems. It explores and develops advanced security mechanisms, encryption techniques, and access controls to protect data from unauthorized access, data breaches, and privacy violations. Trust and Confidence: Research in data protection contributes to building trust and confidence in cloud computing. By developing robust security solutions and demonstrating their effectiveness, research helps alleviate concerns about data security and privacy, fostering greater adoption of cloud services by organizations and individuals. Compliance and Regulations: Cloud computing often involves compliance with data protection regulations and industry standards. Research in this area explores the legal and regulatory aspects of data protection in the cloud and helps organizations understand and comply with relevant requirements. Data Resilience and Recovery: Research in data protection focuses on ensuring data resilience and developing efficient data recovery mechanisms in the cloud. It explores backup and disaster recovery strategies, data replication techniques, and data loss prevention methods to minimize downtime, recover data promptly, and maintain business continuity in the event of system failures or disasters. By addressing these research areas, studies on data protection in the cloud environment contribute to enhancing security, privacy, compliance, and resilience in cloud computing. They provide valuable insights, practical solutions, and guidelines for organizations and service providers to protect data effectively and maintain the trust of users in cloud-based services. The weighted product method approach is commonly used to choose the best data protection in cloud environment. CCSS1, CCSS2, CCSS3, CCSS4, CCSS5 data visibility, data integrity, Maintains compliance, Data security, Data storage. From the result it is seen that CCSS2 got highest rank whereas CCSS5 got lowest rank According to the results, CCSS2 was ranked first.
Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Reviewz
Authors: K. Maithili Et al.
Published: December 2023

The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databases on credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tactics. Machine learning algorithms have thus developed into a powerful tool for real-time unsupervised learning, and anomaly detection., is then explored in detail in order to accurately identify fraudulent transactions. Furthermore, we explore the various data features utilized by machine learning algorithms, including transaction history, transaction amounts, merchant information, and geographical locations. “For people, companies, and financial institutions, A significant financial danger is credit card fraud. In order to detect theft, robust methods for machine learning must be developed. researchers can help minimize financial losses associated with fraudulent activities”. In this research we will be using weighted product method. Taken as Alternative parameters is “Fraud detection using Game theory for M1, Hybrid Approach For Fraud Detection Using Svm And Decision Tree for M2, Fraud Detection Using Som & Psofor M3, Dempster Shafer Theory Along With Bayesian Learning For Detecting Fraudfor M4, Cardwatchfor M5”. Taken as Evaluation parameters is “Sum of Squared Error, Mean Squared error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy”. Model 1 outperformed the other 4 models when a machine learning algorithm was used to identify credit card frauds. With Weighted Product Method we are able to find the best way of detection of credit card frauds by machine learning algorithm which has been evaluated with various parameters and methodology.
Evaluation of Elderly Fall Detection Systems using Data Analysis
Authors: Ramakrishna Reddy Et al.
Published: December 2023

As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries. Modern technology, such as the Elderly Fall Detection System, have been created to solve this issue. This technology attempts to detect falls quickly and react to them, offering aid right away and maybe saving lives. These studies identify a fall when the tri-axial accelerometer reading from a wearable device exceeds the predetermined threshold. Less complexity and computational expense compared to other approaches is one of the main benefits of adopting threshold-based methods. Finding the right threshold value, however, to accurately identify all falls without confounding them with certain ADL, has proven to be a challenging issue. Early Intervention and Medical Aid: Being independent and able to live in one's own house are two things that many elderly people enjoy. Support for carers and peace of mind: detection systems not only aid the elderly but also provide their carers peace of mind. Cost-cutting: Falls among the elderly frequently lead to hospital stays, rehab, and higher healthcare expenses. The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors. It is an easy and obvious method that enables decision-makers to weigh the relative weight of many variables and make wise decisions. Smart surveillance system, Smart cane, Smart Carpet, Smart phone/watch and app. Unobtrusiveness, reliability, privacy, Cost. As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors Unobtrusiveness, reliability, privacy, Cost, smart phone is the highest and smart carpet is the lowest
Design Optimization of Solar Power Inverter
Authors: Sridhar P Et al.
Published: December 2023

Optimizing the design of solar power inverters aims to improve efficiency, dependability, and performance. Effective circuit design, component selection, and advanced power electronics design are all involved. Effective methods for trouble identification and heat control guarantee long-term dependability. Complex control algorithms and filtering techniques enable the generation of high-quality AC output. Different system sizes can be integrated thanks to scalability. Monitoring and communication features enable remote control and real-time performance monitoring. Safety features adhere to all relevant standards and laws. For the highest return on investment, cost-effective design selections balance performance and affordability.  Check to see if any specific aspects, such as dependability, power quality, scalability, cost-effectiveness, or efficiency, need to be improved. data collection Collect all necessary data, such as details on solar panels, load specifications, grid standards, environmental factors, and any other details that might affect the design. Modelling and simulation an electronic or mathematical model of the solar power inverter system should be created. This model should be able to accurately represent the inverter's electrical properties, control structures, and operating behavior. Utilise simulation tools to assess the system's performance under various conditions. Utilizing optimization techniques, algorithms are used to discover the optimal design solution. These algorithms could be based on methods for machine learning, evolutionary algorithms, or mathematical optimization methods. Define the objective function and constraints of the optimization process. Iterative Refinement for Modify the design in response to the optimization's results. Iterate through the design exploration, performance evaluation, and optimization stages to reach a superior design solution. The parameters of the model are continuously updated and modified as required. Taken as alternate parameter is Air Conditioner, Fan, Iron Box, Laptop Charger, Microwave Oven, UPS, Cooler. Taken as evaluation parameter is Battery, Filter capacitor (C1),      Filter inductor (L1), Switching Frequency, DC link Capacitor (C2) 2 nos.
Optimized Deep Learning Schemes for Secured Resource Allocation and Task Scheduling in Cloud Computing - A Survey
Authors: Thirumalai Selvan Chenni Chetty Et al.
Published: December 2023

Scheduling involves allocating shared resources gradually so that tasks can be completed within a predetermined time frame. In Task Scheduling (TS) and Resource Allocation (RA), the phrase is used independently for tasks and resources. Scheduling is widely used for Cloud Computing (CC), computer science, and operational management. Effective scheduling ensures that systems operate efficiently, decisions are made effectively, resources are used efficiently, costs are kept to a minimum, and productivity is increased. High energy consumption, lower CPU utilization, time consumption, and low robustness are the most frequent problems in TS and RA in CC. In this survey, RA and TS based on deep learning (DL) and machine learning (ML) were discussed. Additionally, look into the methods employed by DL-based RA and TS-based CC. Additionally, the benefits, drawbacks, advantages, disadvantages, and merits are explored. The work's primary contribution is an analysis and assessment of DL-based RA and TS methodologies that pinpoint problems with cloud computing.
An Efficient Machine Learning Approach for Prediction of Conjunctiva Hyperemia Assessment using Feature Extraction Methods
Authors: Savita Bamal Et al.
Published: December 2023

The human eye is one of the most intricate sense organs. It is crucial to protect your eyes against several eye disorders that can cause vision loss if untreated in order to maintain your ability to see well. Early detection of eye diseases is therefore crucial in order to prevent any unintended consequences and control the diseases continued progression. Conjunctivitis is one such eye condition that is characterized by conjunctival inflammation, resulting in symptoms like hyperemia (redness) due to increased blood flow. With the aid of the best treatments, modern techniques, and early, precise diagnosis by professionals, it can be cured or can be greatly reduced. The proper diagnosis of the underlying cause of visual problems is frequently postponed or never carried out because of  shortage of diagnostic experts, which leads to either insufficient or postponed corrective treatment. In order to diagnose and evaluate conjunctivitis, segmentation methods are essential for locating and measuring hyperemic regions. In the present study, segmentation techniques are applied along  with feature extraction techniques to provide an effective machine learning framework for the prediction of eye problems. Using the discrete cosine transform (DCT), the segmented regions of interest are converted into feature vectors. These feature vectors are then used to train machine learning classifiers, including random forest and neural networks, which achieve a promising accuracy of 95.92%. This approach enables ophthalmologists to make more objective and accurate assessments, aiding in disease severity evaluation.
Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies
Authors: Ranjeet Kumar Rai Et al.
Published: December 2023

Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy.
FTDA: Performance Enhancement of WSN using Fuzzy based Traffic Data Analysis
Authors: Anuradha P. Gharge Et al.
Published: December 2023

In the present time in Wireless Sensor Network plays an essential role in the monitoring of different physical phenomena. Monitoring of city traffic data analysis is very important in different metro cities due to rapid increase in population.  This research work proposes a model for traffic data analysis using wireless sensor network  incorporated with fuzzy technique. The proposed model is tested for performance parameters such as node dead rate , data packed received. The proposed model improved the efficiency compared to existing techniques of the WSN network for traffic data collection and analysis.
Exploring Quantum Machine Learning Algorithms for Enhanced Data Analysis
Authors: Lalit Mohan Trivedi Et al.
Published: December 2023

The rapid increase of records generation throughout diverse domain names has necessitated the development of superior records analysis techniques. Quantum gadget mastering (QML) has emerged as a promising paradigm that harnesses the computational power of quantum systems to beautify information evaluation duties. This studies paper explores the software of quantum machine getting to know algorithms to cope with demanding situations in information analysis, highlighting their capacity for progressed performance and scalability. We provide an overview of key QML algorithms, talk their blessings, and gift case research demonstrating their effectiveness in actual-global statistics evaluation eventualities. Finally, we outline the cutting-edge kingdom of QML research and speak destiny directions for its software in records evaluation.
Industrial Internet of Things: An Analysis of Emergence, Component and Challenges
Authors: Reena Hooda Et al.
Published: December 2023

Industry 4.0 brought a solution to disrupted industrial manufacturing powered by AI, machine learning, IoT, Cloud Computing, and Wireless Technologies. The factories turn into smart industrial units where the processes are automated, self-reliant, continuously monitored, and scaled as per the observed conditions. The Industrial Internet of Things (IIoT) solves the issues by real- time decision making or even alarming the hazardous situations say breaking down of systems or processes based on predictive analytics. From the raw material to the customer satisfaction the whole production line turns intelligent making the production fast, quality-based, well maintained, monitored, and innovative. The 3D printing, simulation, and augmented reality encourage innovations, designing & testing of customized products in the virtual environment. Big data analytics and accurate prediction not only foster manufacturing quality & quantity but also attracts high-valued staff with increased efficiency. Getting trustworthy data in real-time and eliminating life hazards in monitoring the typical processes are the key attractions of Industry 4.0 for instance, IoT with different kinds of sensors replace human monitoring and even performs well where the humans cannot go like the radioactive, humidity pressure. The product is tested well, safer to use, and promoted to the areas predicted by the advanced computing algorithms in data analytics. However, emerging technology can pose challenges for employers in a country where unemployment exists. So technological advancements, training, and skill developments are urgent areas to look after. This paper presented literature survey focuses on the major components, characteristics and associated challenges of Industrial IoT.
A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System
Authors: Deepa Mane Et al.
Published: December 2023

Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, the combination of road scene-graph representations and graph learning techniques has demonstrated superior performance compared to cutting-edge deep learning methods across various tasks such as action classification, risk assessment, and collision prediction. It's a grueling problem due to large variations under different illumination conditions. Transfer learning is a promising approach to address this challenge. Transfer learning involves leveraging pre-trained deep learning models on large-scale datasets to develop efficient models for new tasks with limited data. In the context of ITS, transfer learning can enable accurate scene understanding with less data by reusing learned features from other domains.

This paper presents a comprehensive overview of the application of transfer learning for scene understanding in cross domain. It highlights the benefits of transfer learning for ITS and presents various transfer learning techniques used for scene understanding. This survey paper provides systematic review on cross domain outdoor scene understanding and transfer learning approaches from different perspective, presents information on current state of art and significant methods in choosing the right transfer learning model for specific scene understanding applications.
PMP-SVM: A Hybrid Approach for effective Cancer Diagnosis using Feature Selection and Optimization
Authors: Pinakshi Panda Et al.
Published: December 2023

Cancer disease is becoming a prominent factor in increasing the death ration over the world due to the late diagnosis. Machine Learning (ML) is playing a vital role in providing computer aided diagnosis models for early diagnosis of cancer. For the diagnosis process the microarray data has its own place. Microarray data contain the genetic information of a patient with a large number of dimensions such as genes with a small sample such as patient details. If the microarray is directly taken without reducing the dimension as the input to any ML model for classification, then Small Sample Size is the resulting issue. So, size of the microarray data needs to be reduces by using either of dimensionality reduction technique or the feature selection technique to increase the model’s performance. In this work, proposed a hybrid model using Principal Component Analysis (PCA), Maximum Relevance Minimum Redundancy (MRMR), Particle Swarm Optimization (PSO) and  Support Vector Machine (SVM) for cancer diagnosis. PCA and MRMR is used for feature selection and PSO is applied to get the optimized feature set. Finally, SVM is applied as the classification model. The proposed model is evaluated against multiple cancer microarray datasets to measure the performance in terms of accuracy, precision, recall, and F1 score. Result shows that proposed model performs better than existing state of art model.
A Lightweight Security Model using Delta Probabilistic Hashing Technique for Secured Data Transmission in IoT Systems
Authors: Prasanna Kumar M Et al.
Published: December 2023

Secure data transmission is one of the most pressing concerns for resource constrained IoT devices. There is a need for an efficient and lightweight solution for data security in IoT applications.  The proposed study is primarily concerned with the creation of a lightweight data security model for an IoT data transmission-based application for data transmission and storage in smart cities. The fundamental contribution of the proposed security model is the creation of a distinct hashing algorithm based on the specific pattern of the previous sequential change in value. The Delta Probabilistic Hashing (DPH) based architecture of key extraction technique for data security will perform this. Based on the hash key signature approach, this kind of safe data handling procedure provides a random key for data encryption, which will simplify the model and speed up the algorithm's operation. Our approach's main features are enhanced efficiency and throughput, lightweight data security, and adaptive key generation.
Quantum Computing Algorithms for Solving Complex Mathematical Problems
Authors: Lalit Mohan Trivedi Et al.
Published: December 2023

The power of quantum mechanics, that is too complex for conventional computers, can be solved by an innovative model of computing known as quantum computing. Quantum algorithms can provide exponential speedups for some types of problems, such as many difficult mathematical ones. In this paper, we review some of the most important quantum algorithms for hard mathematical problems. When factoring large numbers, Shor's algorithm is orders of magnitude faster than any other known classical algorithm. The Grover's algorithm, which searches unsorted databases much more quickly than conventional algorithms, is then discussed.
Mathematics Infographics in STEM Learning for University Students
Authors: Angel Salvatierra Melgar Et al.
Published: December 2023

The aim of the study was to reflect on the role of infographics as a visual medium during the experimental actions of learning achievement with STEM. For this purpose, the qualitative-phenomenological research design was followed, which allowed the hermeneutic understanding of the behavior and reactions of 16 students enrolled in the subject of Euclidean Geometry. Through interviews and a field notebook kept for five weeks, they detected categories such as contextualized scenarios, learning expectations, interdisciplinarity, methodological sequence, resolutions of situations, and evaluation processes, and reached the conclusion. The infographic allowed the visual systematization of the procedures with STEM during the experimentation, in addition, it was an opportunity for the student to show their creativity, imagination, critical judgment, and creative and social skills with interactivity for common objectives, as well as promote multidisciplinary work with common points.
Sentiment Analysis of Post-Covid Patients on Social Media by Predictive Modeling
Authors: S. Roja Et al.
Published: December 2023

Social media has become a powerful platform for individuals to express their thoughts, experiences, and emotions. With the advent of the COVID-19 pandemic, social media has witnessed a surge in posts related to the virus and its aftermath. In India, a country severely hit by the pandemic, there is a prevalence of post-COVID patients sharing their experiences on social media. These posts provide valuable insights into the physical and emotional challenges faced by individuals after recovering from COVID-19. This paper aims to explore the prevalence of post-COVID patients on social media in India, the signs and symptoms experienced by these individuals, the correlations between post-COVID sentiment and patient characteristics, and the development of a predictive model for sentiment analysis. The prevalence of post-COVID patients on social media in India is evident through the increased number of posts related to COVID-19 and its aftermath. The surge in COVID-19 tweets was followed by an increased number of posts expressing personal experiences with the virus, indicating a significant presence of post-COVID patients on social media. These posts provide a valuable resource for understanding the challenges faced by individuals after recovering from COVID-19. Post-COVID patients often experience a range of signs and symptoms that can have a significant impact on their physical and mental well-being. Fatigue, shortness of breath, brain fog, chest pain, headache, and other symptoms have been reported as potential long-term effects of COVID-19. Additionally, patients with post-COVID-19 depressive symptoms share psychopathological symptoms, indicating the presence of mental health challenges. These signs and symptoms highlight the need for a comprehensive understanding of the post-COVID experience and the emotional toll it can take on individuals. Predictive modeling plays a crucial role in analyzing sentiment expressed by post-COVID patients on social media. Predictive modeling is a mathematical process that aims to predict future events or outcomes by analyzing relevant historical data. By developing a predictive model for sentiment analysis, researchers can gain valuable insights into the emotional experiences of post-COVID patients and potentially identify patterns or trends. This modeling approach allows for a systematic analysis of the vast amount of data available on social media platforms.
A Federated Consensus for Proof of Authority in IoT-Blockchain Applications
Authors: Selvakumar R. , Shibu S. , Joy R. Priscilla , R Roopa Chandrika , Kumar C. Ramesh , Kamatchi S.
Published: December 2023

The growing adoption of Internet of Things (IoT) devices and the need for secure and scalable blockchain applications pose significant challenges in the realm of consensus protocols. This paper proposes a novel consensus mechanism called Federated Consensus for Proof of Authority (Fed-PoA), which combines the advantages of Proof of Authority (PoA) and federated learning to achieve secure and scalable IoT-Blockchain applications. The Fed-PoA ensures efficient data sharing, privacy preservation, and decentralized operation. Performance evaluation of this model in a simulated environment demonstrates superior convergence and memory usage compared to a representative work in this context.
Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks
Authors: Prasad M. , Rani P R Sudha , PBV Raja Rao , Sree Pokkuluri Kiran , Murty P T Satyanarayana , Mallesh A. Satya , Babu M. Ramesh , Ramana Chintha Venkata
Published: December 2023

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency.
Cervical Cancer Prediction using NGBFA Feature Selection Algorithm and Hybrid Ensemble Classifier
Authors: CH. Bhavani Et al.
Published: December 2023

Cervical Cancer (CC) is a substantial reason of death midst middle-aged women throughout the world, specifically in developing countries, with approximately 85% of deaths. CC patients can be healed if spotted in the early stages. As no symptoms appear in the initial stages, it has become a challenge for investigators to predict the disease in the early stages. Several machine learning algorithms have been used to predict CC since the last decade. Instead of using a single classifier for the prediction, ensemble methods give accurate results, creating and combining multiple models to produce improved results. In this study, we built a hybrid ensemble classifier, 'A Robust Model Stacking: A Hybrid Ensemble,' in which a homogenous ensemble will be performed on a pool of classifiers in the base level followed by a heterogenous ensemble using the majority voting (soft) algorithm to get the final prediction of the new data. The dataset used in this study contains 858 instances with 32 features built from the risk factors and four targets made from the CC diagnosis tests. We have solved the data imbalance problem using an oversampling technique called SMOTE. The model's efficiency was evaluated based on the accuracy, recall, f1-score, precision, and AUC-ROC curve metrics for all four target variables in the dataset. The proposed Biopsy method's accuracy is 98%, Hinselmann is 97%, Schiller is 96.09%, and Citology is 93%. We implement ensemble learning in this study to increase prediction accuracy and decrease bias and variance. We carried the experiments out using the Python language in Google Colab and Jupyter notebooks. The experimental results revealed that our proposed hybrid ensemble learning records a remarkable accuracy for all four target variables.
Advances in Sentiment Analysis in Deep Learning Models and Techniques
Authors: P. Vijaya Lakshmi Et al.
Published: December 2023

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation.
Bidirectional LSTMs with Byte Pair Encoding in NMT for CLIR using English and Telugu Parallel Corpus
Authors: B N V Narasimha Raju Et al.
Published: December 2023

The Neural Machine Translation (NMT) is very crucial for Cross-Lingual Information Retrieval (CLIR). NMT is effective in translating English language queries to the Telugu Language. In this paper, we are translating English queries to Telugu. The NMT will utilize a parallel corpus for translations. Telugu is a resource-poor language, it is very difficult to supply large amounts of parallel corpus to NMT. So the NMT will have a problem called Out Of Vocabulary (OOV). To overcome this problem Byte Pair Encoding (BPE) is used along with Long Short Term Memory (LSTM), which segments the rare words into sub-words and tries to translate the rare words. It still faces problems like Named Entity Recognition (NER). Some problems of NER can be solved by utilizing bidirectional LSTMs in sequence-to-sequence models. The bidirectional LSTMs (BiLSTMs) will be helpful in training systems in both directions for recognizing the named entities. The accuracy parameters and a BLEU score show the translation quality of NMT with bidirectional LSTMs has slightly more accuracy than regular LSTMs which is considerable.
Examination of Wireless Body Area Network Using Mobile Sinks
Authors: Preeti Nehra Et al.
Published: December 2023

In the last few years, postural mobility has been seen as a significant barrier to the successful deployment of Wireless Body Area Networks (WBAN), and several mobility models have been put out to overcome this problem. Due to the sink node's fixed location in such topologies, WBAN performance was declining. The domains of other wireless networks including MANET, VANET, and FANET have successfully used Mobile Sink as a solution. The network lifespan and other QoS metrics like average end-to-end latency, PDR, throughput and energy consumption are significantly influenced by sink mobility. The random movement of the sink node is taken into account in this study effort to cope with the heterogeneity of network nodes and their movement pattern. Static and mobile sinks (Controlled and Random movements) are used to test both mobility models using Network Simulator NS2.35, and it was shown that mobile sinks improved WBAN performance for all QoS criteria.
Distribution of Heterogeneous Traffic Resources for 5G Cognitive Radio Networks based on Cooperative Learning and QOE
Authors: Pravin M Et al.
Published: December 2023

In addition to offering faster speeds and lower latency, 5G networks provide enhanced availability, significantly higher capacity, improved stability, and superior connectivity. The Mean Opinion Score (MOS), which quantifies the subjective quality of the user experience, has emerged as a widely accepted metric for assessing the quality of various types of network traffic. As we move towards the 5G era, measuring end-user quality has become increasingly important in the evolution of wireless communications. This paper presents a resource allocation approach for 5G cognitive radio networks that leverages cooperative learning and prioritizes Quality of Experience (QoE) for integrated heterogeneous traffic. The solution is based on a distributed underlay Dynamic Spectrum Access (DSA) system that utilizes MOS as a foundational metric for managing resource allocation across real-time video and data traffic, each with distinct characteristics. The proposed technique is designed to meet strict interference limitations for primary users while simultaneously maximizing the overall MOS. This is accomplished by employing reinforcement learning in a system where primary users and secondary users share the same frequency band of interest, ensuring efficient coexistence. Importantly, MOS serves as a standardized measure that allows for training across nodes carrying various types of traffic without compromising performance.
CAD-CDN: Coronary Artery Disease Prediction Using Convolutional Neural Network with Modified Densenet
Authors: Ramasamy MaheshKumar Et al.
Published: December 2023

Atherosclerosis is a synonym for coronary artery disease (CAD), a non-communicable cardiovascular disease. Coronary artery disease, cancer, and tumour illness pose significant human risks. Predicting coronary artery disease (CAD) is a difficult and time-consuming task in the medical field. Early prediction is a virtuoso skill in the medical area, particularly in the cardiovascular sector. Prior research on developing early prediction models provided a grasp of modern strategies for detecting variance in medical imaging. Cardiovascular disease prevention may be accomplished with a diet plan established by the concerned physician after early diagnosis. We proposed a CAD-CDN framework for coronary artery disease prediction using a Convolutional neural network (CNN) with modified densenet. The datasets are collected from the Kaggle repository, and the data normalization has been done with Affinity propagation with an adaptive damping factor (APADF). The best features are selected using ACO with SA as the Hybrid method. Finally, the classification was done with CNN with modified Densenet.  The experimental result has been done with various existing algorithms and proposed one. And the results have shown performance indicators including accuracy, precision, sensitivity, specificity, and measure value.
Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review
Authors: Mrs. Deepali K. Jadhav Et al.
Published: December 2023

V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning.  Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing.  Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging.  When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care.  It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work..