bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilizing J48 Algorithm in Predicting Students Dropout in Higher Education Institution
Authors: Noreen B. Fuentes Et al.
Published: January 2024

Dropout refers to students who voluntarily withdraw from a course or program prior to completion. University dropouts continue to be a major concern for educators and represent a substantial loss of human resources for society. At Cebu Technological University, it is always a challenge of the Department Chairperson the declining student population, which resulted in the reduction of the number of sections per year level and under loading of faculty.

This study centers on the creation of a student-dropout model that predicts a student's behavior toward his studies. This model utilized the J48 decision tree algorithm, which extract data from the Student Information System (SIS) portal of the existing institution. Nine hundred sixty-one (961) demographic and academic datasets from students enrolled in the two programs under the College of Computer, Information, and Communications Technology (CCICT) of Cebu Technological University (CTU) with nineteen (19) attributes. During the testing procedure, 10-fold cross-validation was utilized. The J48 pruned tree utilized an average of 3 foliage with 4 as the measure of the tree.  The Kappa statistic yields a value of 0.8617 and its Correctly Classified Instances rate of 93.7%. This algorithm helps a lot to the institution in reducing the escalation of the attrition rate and providing proactive measures to address the issue.
Argumentation Mining System for Corpus-based Discourse Analysis based on Structured Arguments
Authors: Shashi Prabha Anan Et al.
Published: January 2024

An Argumentation mining system can analyze a large volume of text data through a variety of sources. Nowadays it is highly useful in the areas of business, economics, and finance with digital marketing being the most promising field along with social media. It is the study of corpus-based discourse analysis that involves the automatic identification of argumentative structure in text. Initially, AM talks about extracting structured arguments from natural text, often unstructured or noisy text. Theoretical approaches of AM and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable of adjusting to argumentation conditions that exist in social media. In this scenario it is a very challenging argumentation scheme able to identify the distinct sub-task and capture the needs of social media text, revealing the need for adopting a more flexible and extensible framework. Corpus-based Machine Learning of linguistic annotations has enabled researchers to identify repetitive linguistic patterns of language use and to uncover hidden meaning in all areas of Natural Language Processing.
Design a New Neural Network Architecture Using a Layer of Neurons
Authors: Ibrahim Altarawni Et al.
Published: January 2024

Nowadays there are many different models of artificial neural networks. The difference between these models lies in the learning methods only, that is, in the rules for changing the parameters of the algorithm tuning, with or without links and side comments. While studying the general framework of network models, we see that the rules for obtaining the result and the mechanism for calculating the error can differ. For example, a multilayer realization might produce a threshold function when used as a classifier, or a linear function if used as an internal typeface.

Where this research came to discuss the possibilities of the standard representation of some models of artificial neural networks, which clarify and treat some of the characteristics of that representation. Which can be considered an essential element in the process of typical representation of these networks, where a new proposal is made during this representation by using a “layer” of neurons, in other words, using a group of neurons that work in parallel and perform the same functions for which they were set .
The Dangers of Public Wi-Fi: How to Protect Your Device Through Cyber Security
Authors: Rajan Gupta Et al.
Published: January 2024

The public Wi-Fi networks become more and more common, there are numerous risks to personal cybersecurity. This article explores the hidden risks users encounter when connecting to these public networks, including malware distribution, surveillance, and attacks known as man in the middle. It also looks at doable standards for cybersecurity along with measures, like turning on firewalls and using virtual private networks (VPNs), that can help reduce these risks. There is an emphasis on the possible consequences of cybersecurity breaches, which can include anything from data theft to monetary losses. The article also looks at the statutory and regulatory structures that control the security of public Wi-Fi. Lastly, it points out gaps in the body of knowledge, opening the door for more studies and suggestions in this important area. This research looks at the potential dangers of utilising public Wi-Fi and offers workable cybersecurity recommendations for protecting mobile devices. The research is organised around three main topics such as security flaws in public Wi-Fi networks, the success of various cybersecurity methods, and the importance of educating and protecting end users. The first topic discusses how to spot and assess dangers like eavesdropping and man-in-the-middle attacks when using public Wi-Fi. The effectiveness and usefulness of VPNs and encryption techniques are evaluated in the second topic. The third principle stresses the value of advising users on how to keep their Wi-Fi connections safe and secure through consumer education. This research attempts to provide a thorough comprehension of device security in public Wi-Fi settings through thematic secondary data analysis. The results and suggestions are meant to provide consumers with the tools they need to safeguard their devices in advance, leading to a more secure time spent online.
Evaluation of Lung Function by Spirometry in Textile Mill Workers
Authors: Pradip H. Kadam Et al.
Published: January 2024

Background: In present scenario industrialization is growing at a faster rate throughout the world in developed as well as developing countries. Occupational lung disease is becoming one of the major problem in occupational health. Many environmental problems are associated with textile industry such as water pollution, noise pollution, soil pollution and air pollution. Out of these different pollutions air pollution resulting from cotton dust is the most important factor affecting health of the workers. Various studies in India and outside have been carried out in cotton mill workers with different results. So present study was planned to evaluate the lung function of cotton mill workers by spirometry in this part of country.

Material & methods: The study was carried out in 55 participants who were exposed to cotton dust directly. 55 controls were also selected from the workers of same mill but not exposed directly to cotton dust. Spirometry findings were compared between two groups.

Results: The values of most of the PFT parameters were significantly reduced in subject group compared to control group (p<0.05). However there was no any significant difference in the values of FEV1/FVC ratio between the groups (p>0.05).

Conclusion: From our study we conclude that workers in the cotton mills are exposed to cotton dust and various types of other air pollutants. And the chronic exposure results in decline in pulmonary function
Performance Evaluation of Deep Learning Autoencoder in Single and Multi-Carrier Systems
Authors: Srinivasa Rao Kandula Et al.
Published: January 2024

An entire system of Single Carrier Communication and Orthogonal Frequency Division Multiplexing is modelled using an autoencoder. The model employs Deep Neural Networks (DNNs) as the transmitter and receiver, responsible for tasks such as encoding, modulation, demodulation, and decoding. The effectiveness of this approach is demonstrated by its ability to outperform traditional communication systems in real-world scenarios that involve channel and interference effects, as measured by the Block Error Rate. AI-enabled wireless systems can overcome limitations of traditional communication systems by learning from wireless spectrum data and optimizing performance for new wireless applications. The aim of this paper is to examine how autoencoder-based deep learning can enhance the performance of a communication system that employs Single Carrier and OFDM. The architecture effectively addresses channel impairments and improves overall performance. The simulation results suggest that even when the autoencoder's channel layer is affected by impairments, autoencoders still outperform traditional communication systems in terms of BLER performance.
A Novel Approach for Detecting Outliers by Using Isolation Forest with Reducing Under Fitting Issue
Authors: Prashant M Goad Et al.
Published: January 2024

The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during the training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is good knowledge that running time for ML tasks is significantly impacted by conditions as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection of connected supervised learning for binary classification. We carry out in-depth tests on various datasets and demonstrate that in our latest outlier finding technique compare with the old way. Our approach yields superior outcomes in terms of accuracy, precision, recall & F-1 score. Additionally, we successfully lowered the machine learning algorithms' under fitting issue.
Cyber Crimes in the State of Tamilnadu with Special Reference to Investigation Process
Authors: Raajavinayaga Subaash Et al.
Published: January 2024

The society and its needs changes with the time therefore the criminal law is required as per the situation. Thus the prime object of criminal law is the protection of the public by the maintenance of law and order in every situation even in the age of information technology. The progress of civilization is evidenced by the everchanging information technology, which is easily accessible by use of computers and no doubt, put to use for improvement in the living ofHuman Beings. Information Technology has made improvements in every aspect of human life such as education, industry, commerce, governance, personal lifestyle, and social life around the world.Crime is a social and economic phenomenon and is also old as human society. Crime is a legal concept and has the sanction of the law Crime or an Offence is a wrong that can be followed by criminal proceedings which may result in punishments. The hallmark of criminality is that it is a breach of criminal law. As per Lord Atkin “ the criminal quality of an act cannot be discovered by reference to any standard but one: is the act prohibited with penal consequences”. Many of the cyber-crimes penalized by the IPC and the IT Act have the same ingredients and even nomenclature. The punishment prescribed for offenses under sections 67A and 67B of the IT Act is on first conviction, imprisonment of either description for a term which may extend to 5 (five) years, to be accompanied by a fine which may extend to Rs. 10,00,000 (Rupees ten lac) and in the event of second or subsequent conviction, imprisonment of either description for a term which may extend to 7 (seven) years and also with fine which may extend to Rs. 10,00,000 (Rupees ten lac).
Street Light Controller Including Automatic Traffic Light Controller System Implementation on FPGA
Authors: Ajaykumar Dharmireddy Et al.
Published: January 2024

— The number of vehicles has greatly expanded over time and is continually growing. Due to traffic congestion, a sizable amount of working hours must be spent on the roads, which has an indirect negative influence on the economy and leads to preventable road accidents that end in fatalities. While metropolitan infrastructures are developing much more slowly, the number of vehicles is rising. We require a system that can successfully manage such a circumstance. A system that can efficiently manage traffic is what we call an automatic traffic control system. Moreover, a system for automatic street lights is built within. This suggested concept tries to automate traffic regulation based on density and a controller for automatic street lights that only come on at night. It detects vehicles by using IR (Infra Red) sensors in every direction. IR sensor modules for traffic lights and an LDR sensor module for streetlights provide the inputs, respectively. Verilog HDL is used to implement the solution, and Xilinx Vivado is used to simulate it. An FPGA has this suggested model pre-programmed (Field Programmable Gate Array).
Batteries on the Move: Navigating Challenges, Expanding Horizons for Indian EVs
Authors: Shantam Babbar Et al.
Published: January 2024

In India, the widespread adoption of swappable battery systems for electric vehicles faces significant hurdles such as regulatory gaps, infrastructure limitations, technological constraints, economic uncertainties, and environmental complexities. Overcoming these challenges demands a cohesive strategy involving thorough regulatory assessment, infrastructure expansion, tech collaboration, financial scrutiny, and environmental evaluations. This holistic approach is key to unlocking the untapped potential of swappable batteries, paving the way for an innovative, eco-friendly electric mobility landscape in India. This research paper aims to provide valuable insights for policymakers, industry players, and researchers grappling with the adoption of swappable battery systems in India's EV sector.
Analysis of Vision based Techniques for the Translation of Indian Sign Language
Authors: Attili SubhaVidisha Et al.
Published: January 2024

Sign language acts as a medium of communication among those of the hearing impaired and mute community. However, it cannot be easily understood by common people. Various research has been done to bridge this gap by developing Sign Language Recognition (SLR) methodologies. Studies say that 1 in every 5 deaf people is Indian.

In this paper, a thorough review of these methodologies has been done, to compare and contrast various aspects of them. This includes an overview on different preprocessing methods used like segmentation, image morphological processing, cropping, etc, feature extraction techniques like Fourier Descriptors, Image Moments, Eigen values, Mediapipe and others. This study also covered classification models spanning from Distance metrics to Kernel based approaches and feedforward neural networks, along with Deep Learning based methods such as CNNs, LSTMs, GANs, Transformers etc.
Electronic Health Record System using Blockchain Technology
Authors: Anchala Balaraj Et al.
Published: January 2024

The healthcare sector is frequently known for being delicate and intricate.Individuals' sensitive information must be kept safe, secure, and protected.Blocks of the blockchain are secured and bound to each other using cryptographic principles.

By maintaining the patient at the centre of the medical ecosystem system and establishing greater security, interoperability, and privacy of stored patient records, blockchain has the potential to eradicate the problems ailing the industry and transform healthcare.

By decentralizing and encrypting health records, blockchain ensures that patient data is securely stored and tamper-proof. Additionally, blockchain can facilitate the seamless exchange of medical information between different healthcare providers, leading to better coordination of care and reduced medical errors. By leveraging Ethereum's smart contract functionality, healthcare organizations can securely store and

share patient data, ensuring its integrity and confidentiality. Moreover, Ethereum's programmable nature allows for the development of decentralized applications (DApps) that can streamline various healthcare processes, such as medical record management, supply chain tracking, and clinical trials. Overall, the integration of blockchain in the healthcare industry has the potential to revolutionize the way healthcare data is managed, ensuring privacy, security, and efficiency in patient care.
Classification of Classical Indian Music Tabla Taals using Deep Learning
Authors: S. T. Patil Et al.
Published: January 2024

In the research that we are bringing to light, we profoundly explore the categorization of Classical Indian Music Tabla Taals. This emphasizes widely recognized taals such as Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. To push the boundaries of our understanding, we implement a mixed-methods approach tethering both Feedforward Neural Networks (FNN) and Convolutional Neural Networks (CNN). These state-of-the-art technologies enable us to dissect and categorize tabla taals efficiently. In essence, the hallmark of Classical Indian music is its complex and multifaceted rhythms brought to life by the primal percussive instrument - the tabla. The conception and reproduction of these nuanced taals require technical finesse. Thus, accompanying the digital revolution and the eclectic musical preferences, it becomes essential for advanced methodologies to pinpoint and classify tabla taals. The hardcover of our research opens up to the magnificent crafting of an unmatched model employing both FNN and CNN. This blend enables us to recognize diverse features unique to tabla taals like Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. The model obtained its bosom knowledge during training from an assortment of Classical Indian music recordings showcasing these invigorating taals. This fosters a broader understanding regarding the array of minute differences brimming within each rhythmic inheritance. To bring user interaction to life, we have embedded a Graphical User Interface (GUI). This empowers users to introduce an audio file filled with table music from the taals listed and receive on-the-spot recognition. refining their connection and knowledge of the taal in question. Our research findings procure paramount importance in the scape of music analysis, especially framed within the heart of Classical Indian Music. We propose a system that would serve as a tool for amateur table players to learn the skill well and master their art. Instructors could also utilize it for training purposes. It opens a new window of possibilities providing an advanced model for intuitive, swift, and accurate automated identification of tabla taals.
Review Paper on Enhancing Stock Price Prediction with Sentiment Analysis of Bank Nifty Index
Authors: Ravikant Zirmite Et al.
Published: January 2024

Sentiment analysis has emerged as a powerful tool in financial markets, offering the ability to harness market participants' collective wisdom and emotions to make more informed investment decisions. This research paper presents a comprehensive literature review on sentiment analysis models in predicting stock prices, focusing on the Bank Nifty Index, a critical benchmark in the Indian financial sector. The review begins by elucidating the significance of sentiment analysis in stock price prediction, highlighting its pivotal role in capturing market sentiment, investor emotions, and their impact on asset valuations. A detailed overview of sentiment sources, encompassing financial news, social media, reports, and other relevant data streams, provides insight into the rich tapestry of information that sentiment analysis can leverage. The paper delves into various sentiment analysis approaches, from traditional lexicon-based and rule-based methods to cutting-edge machine learning and deep learning techniques. It elucidates the intricate process of integrating sentiment analysis with stock price prediction models, exploring feature concatenation, time series integration, and weighted data to incorporate sentiment insights effectively. Evaluation metrics crucial for assessing the performance of both sentiment analysis models and stock price prediction models are thoroughly discussed. It concludes by highlighting the substantial potential of sentiment analysis in augmenting stock price prediction, offering investors and traders valuable tools to navigate the complexities of financial markets.
Comprehensive Trust based Routing Protocol to Mitigate Black-hole attack in Wireless Sensor Networks
Authors: B.Sandhya Rani Et al.
Published: January 2024

Wireless Sensor Network (WSN) is a collection of sensor nodes, that sense environmental data and send it to the administrator for further processing. Sensor nodes are wireless devices with limited battery and are vulnerable to security attacks such as black hole attack, gray hole attack, sink hole attack etc. Researchers have proposed many security mechanisms to mitigate security attacks. Trust based approaches have gained tremendous interest among researchers to embed security in WSNs. A Trust Based Secure Routing Protocol to mitigate black hole attack is presented in this paper. The protocol computes comprehensive trust value for each node and routes the packets only through the nodes with trust value > 0.5. The results of the proposed protocol are compared with TBSEER [10] protocol. The results show an improvement in Packet Delivery Ratio (PDR), throughput, End to End (EED), routing overhead and energy consumption.
Impact of Interpretation Skills in Geography through Augmented Reality Integrated with Chatbot
Authors: Anireddy Srilakshmi Et al.
Published: January 2024

Technologies like Augmented Reality (AR) and Artificial Intelligence (AI) like Chatbot are widely used in people's lives and work, especially to motivate, collaborate and improve learning skills. Numerous applications have been created to meet needs for daily life and learning as a result of the rising study into the use of augmented reality and artificial intelligence in education. AR is a technology used for better visualization, and a chatbot application developed with AI is used for assistance at any time. Integrating AR and Chatbot would impact increasing the student learning experience in the geography compared to the conventional approach. To support this, a pilot study was conducted for 30 middle school students to study the impact of integrated AR-AI applications on the student’s learning experience in understanding geography concepts.
Revolutionizing Green Transport: An Extensive Review of Hybrid Electric Vehicle Charging Stations and Electric Microgrid Integration
Authors: Kiran Thatikonda Et al.
Published: January 2024

Electric vehicles (EVs), recognized as a strategic approach to reducing oil consumption and greenhouse gas emissions, rely on electricity instead of traditional fuels like petrol or diesel for battery charging, positioning them as a significant player in future energy landscapes. The anticipated decline in oil demand aligns with the increasing prevalence of EVs, making attention to charging infrastructure crucial. This paper extensively explores charging infrastructure considerations, emphasizing their significance in both urban and rural contexts, especially in regions with unstable or absent power supplies. Examining off-grid, grid-connected, and hybrid charging modes, the research delves into various EV designs, including those utilizing fuel cells or batteries.

A thorough understanding of energy-source-based charging techniques and diverse power-level charging stations is presented, catering to readers' interests. With a focus on enhancing the longevity and efficiency of electric vehicles, researchers are investigating innovative charging methods, including microgrid concepts within charging stations. Recognizing electric vehicles as multi-energy systems, the paper underscores the importance of effective power management and control for optimal energy utilization.

Additionally, the review scrutinizes the impact of electric vehicles on utility grid infrastructure & maintenance, evaluating various power management and control systems. This comprehensive review serves as a valuable resource for electric vehicle operators and research engineers, offering insights into the current state of the field and potential avenues for future exploration.
An Improve the Onboarding Process in Trade Finance Applications Using Blockchain Technology
Authors: Ahmed Mohamed Abd El-Wahab Et al.
Published: January 2024

Today, challenges in Know Your Customer (KYC) and Anti-Money Laundering (AML) processes include inefficiencies, data silos, and the risk of fraudulent activities. Integrating blockchain technology offers a transformative solution to these issues. Blockchain's decentralized and tamper-resistant nature ensures a single, verifiable source of truth for customer information, reducing data discrepancies across institutions. Smart contracts can automate AML compliance checks, ensuring real-time monitoring and rapid response to suspicious activities. The immutability of blockchain records enhances auditability, facilitating regulatory compliance. Furthermore, the secure and transparent nature of blockchain instills trust among stakeholders, fostering collaboration in combating financial crimes. By leveraging blockchain in KYC and AML processes, the financial industry can achieve enhanced efficiency, reduced fraud, and strengthened regulatory adherence.
A Study on Sum Divisor Cordial Labeling Graphs
Authors: T. Christy Et al.
Published: January 2024

Cordial labeling refers to a graph  with  vertices and  edges as a sum divisor if there exists a bijection function  such that for each edge  assign the label 1 , if  and 0 otherwise, satisfying the condition  is the number of edges having the label 1 , and  is the number of edges having the label 0 . A graph with sum-divisor cordial labeling is called a sum-divisor cordial graph. In the paper, we establish this alternate triangular belt graph, twig graph, duplication of the top vertex Alternate triangular snake graph, duplication of top vertex Pentagon snake graph, jellyfish  when  and  are even, Spider graph with n spokes .
Securing the Cloud: A Critical Appraisal of Data Security Strategies
Authors: Madhura Yadav M P Et al.
Published: January 2024

Cloud computing, a paradigm shift in the evolution of the internet, has garnered significant attention. However, security remains a primary concern, hindering its widespread adoption. Cloud computing essentially transfers user data and applications to remote data centers, where users relinquish control, and data management practices may not always adhere to the highest security standards. This unique characteristic of cloud computing raises a multitude of security concerns that warrant careful consideration and understanding. One of the most crucial and prevalent security concerns is the potential exposure of user data and applications stored on service provider premises. In this article, an attempt is made to review the literature in this area of research.
Performance Estimation for 5G Millimeter-Wave Communication
Authors: Abhishek Madankar Et al.
Published: January 2024

An outline of Performance characteristics in the fifth way of generation system that is (5G) wireless communication is currently being created more utilize in the frequency range of millimeter wave (mmWave) is provided in this paper. Here, the early findings and core ideas of 5G networks are provided, along with a description of the efforts made by various international groups to simulate the channels for both licensed and unlicensed applications. Path Loss, LOS probability for multiple standards bodies such as (LOS) called a   line-of-sight and further having (NLOS) called as  Non line-of-sight probabilities for the range of frequency of 28 GHz  has been compared.
Exploring Opportunities: Overcoming E-Commerce Challenges for Small and Medium-Sized Businesses in Developing Countries
Authors: Abdulla Prebreza Et al.
Published: January 2024

Small and medium-sized businesses have a significant impact on the economic development of a country. Also, the development of a global market without barriers can provide many opportunities for businesses. In developing countries, e-commerce can be seen as an opportunity for SMEs to have the opportunity to compete with larger businesses, as well as to have access to identifying lower cost sources in the international market.

However, the levels of e-commerce development in SMEs turn out to be lower than the potential that this market offers. The acceptability of new technologies or discoveries is estimated to be lower, which may be a result of limited financial resources and lack of qualified staff. On the other hand, it is estimated that small and medium-sized businesses have a high contribution to the development of a country, so their orientation to new trade patterns is important; for their performance, as well as for the development of the country.
Smart Ontology Framework for Multi-Tenant Cloud Architecture
Authors: Dimpy Jindal Et al.
Published: January 2024

The exponential growth of data complexity in an era marked by the rapid expansion of the computer environment has led to an increase in the demand for scalable and effective systems. The crucial stage of data management, which acts as a vital conduit for accelerating the processing of enormous amounts of data, is at the centre of this paradigm. Scientific workflows must be coordinated in order to orchestrate the management of large datasets within this complex ecosystem. These workflows differ from generic workflows in that they involve a complex interplay of scheduling, algorithms, data flow, processes, operational protocols, and a focused attention on data-intensive systems. Software as a Service's (SaaS) distinctive feature of multi-tenancy is inextricably related to the growth of the industry. In this complex fabric, the investigation of scientific processes reveals a mutually beneficial relationship with the multi-tenant cloud orchestration environment, revealing a realm that goes beyond simple control and data propagation. It opens a fresh path for system development and makes service delivery's previously hidden facets visible. This study pioneers an exploration into a thorough framework for scientific operations within the context of multi-tenant cloud orchestration. Semantics-based workflows, which leverage semantics to help users manage the complexities of data orchestration, form the basis of this paradigm. In addition, policy-based processes provide another level of intricacy, giving users a flexible way to manoeuvre the complex environment of multi-tenancy, orchestration, and service identification. The study focuses on the fundamentals of orchestrating scientific workflows in a multi-tenant cloud environment, where the creative, scalable, and effective composition results from the harmonious integration of data and semantics under the guidance of rules.
Service Control in a Retrial Service Facility System with Two-type of Customer-Semi MDP
Authors: C. Selvakumar Et al.
Published: January 2024

In the literature on service facility system, it is common to assume fixed service rate, such as flexible retrial arrival, this assumption is not realistic. In contrast retrial demands model rarely define the mechanism through which arrival of customers is classified into two types say priority customers and ordinary customers. For the given values of maximum inventory, maximum waiting space, reorder level and lead times, we determine the optimal ordering policy at various instants of time. The system is formulated as a Semi-Markov Decision Process and the optimum policy to be employed is found using linear programming method. Numerical examples are provided to illustrate the model.
Review Paper on Analysis of Human Behaviour on Movie Reviews
Authors: Saksham Sharma Et al.
Published: January 2024

Movie reviews are a frequently employed tool by consumers to gauge whether a movie justifies the cost of admission. However, their actual impact on the consumer thought process varies. Movie reviews serve as a valuable source of understanding human behavior and preferences, offering insights into various aspects of the human psyche. This paper presents a comprehensive review of research that analyzes human behavior within the context of movie reviews. By examining various dimensions of sentiment, opinion formation, bias, and social media, this paper aims to provide a holistic overview of the intricate relationship between human behavior and movie reviews. Through the synthesis of studies conducted across different methodologies and platforms, this review sheds light on the motivations and patterns that drive individuals to express their opinions about movies.
IoT malware detection using a novel 3-Sigma Auto-Funnel Transformer approach
Authors: Moushumi Barman Et al.
Published: January 2024

The proliferation of Internet of Things (IoT) devices has ushered in a new era of connected technologies, but it has also brought significant security challenges, particularly in the area of malware detection. This research paper presents a novel approach, the “3 Sigma Auto Funnel Transformer,” that designed to address the specific complexities of malware detection in IoT devices. By leveraging advanced deep learning techniques and a multi-layered architecture, the proposed framework provides an innovative solution to detect and mitigate malware threats in IoT ecosystems. By combining the precision of the ”3 Sigma” approach with the efficiency of an ”Auto Funnel Transformer,” this architecture achieves superior detection accuracy and performance. Through comprehensive evaluations, this paper demonstrates the effectiveness of the proposed system in bolstering the security of IoT devices, thereby contributing to the ongoing efforts to protect these essential components of our interconnected world.
Sentiment Analysis for Online Product Reviews and Recommendation Using Deep Learning Based Optimization Algorithm
Authors: P. Manjula Et al.
Published: January 2024

Recently, online shopping is becoming a popular means for users to buy and consume with the advances in Internet technologies. Satisfaction of users could be efficiently improvised by carrying out a Sentiment Analysis (SA) of larger amount of user reviews on e-commerce platform. But still, it is a challenge to envision the precise sentiment polarity of the user reviews due to the modifications in sequence length, complicated logic, and textual order. In this study, we propose a Hybrid-Flash Butterfly Optimization with Deep Learning based Sentiment Analysis (HFBO-DLSA) for Online Product Reviews. The presented HFBO-DLSA technique mainly aims to determine the nature of sentiments based on online product reviews. For accomplishing this, the presented HFBO-DLSA technique applies data pre-processing at the preliminary stage to make it compatible. Besides, the HFBO-DLSA model uses deep belief network (DBN) model for classification. The HFBO algorithm is used as a hyperparameter tuning process to improve the SA performance of the DBN method. The experimental validation of the presented HFBO-DLSA method has been tested under a set of datasets. The experimental results reveal that the HFBO-DLSA approach surpasses recent techniques in terms of SA outcomes. Specifically, when compared to various existing models on the Canon dataset, the HFBO-DLSA technique achieves remarkable results with an accuracy of 97.66%, precision of 98.54%, recall of 94.64%, and an F-score of 96.43%. In comparative analysis, other approaches such as ACO, SVM, and NN exhibit poorer performance, while TextCNN, BiLSTM, and RCNN approaches yield slightly improved SA results.
A Machine Learning Approach to Pomegranate Leaf Disease Identification
Authors: Mohammed Saleh Al Ansari Et al.
Published: January 2024

Pomegranate is a fruit with the highest yield and the greatest geographic distribution in Asia. On the other hand, the plants are susceptible to a diverse range of illnesses as a consequence of a number of factors, which leads to the total destruction of the plants and a harvest that is significantly reduced. In order to avoid reductions in agricultural output, it is necessary to diagnose plant diseases as quickly as is practically possible. It is a difficult and time-consuming task to manually monitor the progress of diseases on pomegranate leaves. Therefore, Deep Learning is utilised in the diagnosis of diseases affecting pomegranate trees (DL). The goal of this study is to develop an algorithm for automatically diagnosing diseases affecting pomegranate plants based on images of the plant's leaf structures. The process of a disease detection system includes the gathering of data, the analysis of that data, the categorization of gathered images, and their subsequent deployment. The Mendeley database is utilised in order to generate images of pomegranate leaves in both healthy and diseased states. After that, the original, unaltered raw image is polished. Two different DL models, AlexNet and VGG-16, are put to use in this classification technique. To determine the optimal model, it is necessary to do accurate and loss-oriented measurements. According to the measurements, AlexNet does a good job of recognising diseases that affect leaf tissue. Later, an AlexNet-based smartphone app is developed to assist farmers in performing disease detection on pomegranates without the assistance of professionals. This software is intended to help farmers save time and money.
Efficient Extraction and Automated Thyroid Prediction with an Optimized Gated Recurrent Unit in Recurrent Neural Networks
Authors: Nagavali Saka Et al.
Published: January 2024

Computer-aided tools are becoming increasingly important in medical diagnostics. This paper introduces the Efficient Feature Extraction Based Recurrent Neural Network (FERNN) for computer-aided thyroid disease prediction. The FERNN model uses a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) optimized with the COOT Optimization Algorithm.The study begins by gathering data from an open-source system and preprocessing it using min-max normalization to address missing values. The preprocessed data undergoes a two-level feature extraction (TLFE) procedure. In the first level, a ranked filter feature set technique is used to prioritize features based on medical expert recommendations. In the second level, a variety of metrics, including information gain, gain ratio, chi-square, and relief, are used to rank and select features. A composite measure guided by fuzzy logic is then used to select a judicious subset of features. The FERNN model uses the GRU-RNN to classify thyroid diseases in the databases. To optimise, the COOT optimization method is employed. The model's weights. The FERNN model was put into practise in MATLAB and assessed with a variety of statistical metrics, including kappa, accuracy, precision, recall, sensitivity, specificity, and the F-measure. The proposed methodology was benchmarked against traditional techniques, including the deep belief neural network (DBN), artificial neural network (ANN), and support vector machine (SVM).
Toxic Comment Classification based on Personality Traits Using NLP
Authors: Shaik Sai Rohit Et. al
Published: January 2024

Concerns about the frequency of harmful remarks have been raised by the growth of online communication platforms, which makes it difficult to create inclusive and safe digital spaces. This study explores the creation of a strong framework that uses machine learning algorithms and natural language processing (NLP) methods to categorise harmful comments. In order to improve the accuracy and comprehensiveness of categorization, the study investigates the integration of personality trait analysis in addition to identifying hazardous language. A wide range of online comments comprised the dataset that was gathered and put through extensive preparation methods such as text cleaning, lemmatization, and feature extraction. To facilitate the training and assessment of machine learning models, textual data was converted into numerical representations by utilising TF-IDF vectorization and word embeddings. Furthermore, personality traits were extracted from comments using sentiment analysis and language clues, which linked linguistic patterns with behavioural inclinations. The study resulted in the development and assessment of complex categorization models that combined features from textual content and inferred personality traits. The findings show encouraging associations between specific personality qualities and the use of toxic language, providing opportunities to identify subtle differences in toxic comment contexts. In order to provide insights into developing more sophisticated and successful methods of reducing toxicity in online discourse, this study outlines the methodology, major findings, and consequences of incorporating personality traits analysis into the classification of toxic comments.
Implementing a Hybrid Model using K-Means Clustering and Artificial Neural Networks for Risk Prediction in Life Insurance
Authors: Jeff Kimanga Nthenge Et al.
Published: January 2024

Accurate assessment of policy holder risk is critical for life insurance companies to properly price policies and manage long-term liabilities. However, the complexity of risk factors makes reliance solely on traditional actuarial models inadequate, especially with the proliferation of big data and unstandardized data from diverse sources. This study investigated the development and performance of a hybrid machine learning model combining artificial neural networks (ANN) and K-means clustering for enhanced risk prediction in life insurance underwriting. The exponential growth of unlabelled data presented challenges for predictive modelling. The proposed hybrid model leveraged the strengths of artificial neural networks in modelling nonlinear relationships and K-means clustering in unsupervised for pattern recognition to handle unstandardized data. Using anonymized life insurance application data from Kaggle, the hybrid model was evaluated against the artificial neural network algorithm alone. The results demonstrated that integrating K-means clustering and artificial neural network together with principal component analysis for pre-processing led to superior model performance, with testing accuracy improving from 90% for artificial neural network to 98% for the hybrid technique. Additional metrics like precision, recall and AUC also showed enhancements. The improved predictive capability highlighted the potential of the hybrid approach in transforming legacy underwriting practices towards a more sophisticated data-driven analytical evaluation of policy holder risk. However, limitations existed including the use of single sourced insurance dataset due to data privacy concerns. Further research on integrating diverse algorithms can help insurers unlock more value and gain a competitive edge through advanced analytical modelling and testing on larger real-world datasets. While challenges remain, this study provided key insights into a promising new technique for modernizing risk prediction in the life insurance industry in the era of big data.
Deep Learning for Software Defect Prediction: An LSTM-based Approach
Authors: Prashant Sahatiya Et al.
Published: January 2024

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction..
Responsibility for Offences against Internationally Protected Persons: Contemporary Legal Aspects
Authors: Vylegzhanin A. N. , Kantur R. A.
Published: January 2024

The article examines complex legal aspects of the problem of responsibility, when it comes to offences against internationally protected persons (OIPP). The article reveals that, depending on the international legal qualification of the offence, OIPP can be qualified as either an ordinary crime (the one prosecuted under domestic law following the participation of the State in the relevant international conventions the key of which is the 1973 Convention on the Prevention and Punishment of Crimes Against Internationally Protected Persons, Including Diplomatic Agents) or an international crime when it is a violent act against the protected person committed in the situation of an armed conflict. The authors argue that notwithstanding the fact that both cases entail individual criminal responsibility of the delinquent, individual criminal responsibility for OIPP as an ordinary crime occurs to the extent in which a State party to a relevant international convention has provided for punishment in its national legislation for the conduct criminalized thereby. At the same time, with regard to OIPP as a war crime, the article highlights that the commission of OIPP engenders the right to exercise universal jurisdiction under customary international law and the obligation to exercise quasiuniversal jurisdiction under the “Geneva law” for the purpose of bringing delinquents to justice. In the meantime, the most controversial issue is the question of international legal responsibility of a State in cases of OIPP. It is argued that such responsibility can arise, when OIPP is a war crime committed by a State agent or a non-State actor effectively controlled by the State, as well as when it fails to undertake necessary measures to ensure personal inviolability of protected persons in violation of diplomatic law, or in situations of the denial of justice.