bluuebunny/crossref_metadata_2025 huggingface dataset:
A Smart System for Future Generation based on the Internet of Things Employing Machine Learning, Deep Learning, and Artificial Intelligence : Comprehensive Survey
Authors: C. Ramakrishna Et al.
Published: January 2024

The Internet of Things (IoT) is a networked system including interconnected things, devices, and networks that utilize the internet for communication and data exchange. The entity engages in interactions with both its internal and external surroundings. The IoT is capable of seeing the surrounding environment and responding in a way that is appropriate and adaptive. The utilization of advanced technology in this context enhances the environment and thus enhances the overall well-being of humanity. The IoT facilitates inter-device communication, whether through physical or virtual means. The IoT facilitates the enhancement of environmental intelligence, enabling seamless connectivity across many devices at any given moment. The concepts centred on the IoT, such as augmented reality, high-resolution video streaming, autonomous vehicles, intelligent environments, and electronic healthcare, have become pervasive in contemporary society. These applications have requirements for faster data rates, larger bandwidths, enhanced capacities, decreased latencies, and increased throughputs. IoT and Machine learning (ML) are among the fields of research that have shown significant potential for advancement. ML and IoT are used to build intelligent systems. Those networks will modify the ways in which worldwide entities exchange information. This article gives a comprehensive survey of the upcoming 5G-IoT situation, as well as a study of IoT smart system applications and usages. In addition to covering the latest developments in ML and deep learning (DL) and their impact on 5G-IoT, this article describes a comprehensive study of these important enabling technologies and the developing use cases of 5G-IoT.
Predicting Academic Student Performance based on e-Learning Platform Engagement using Learning Management System Data
Authors: Siti Maisara Murniyati Muin Et al.
Published: January 2024

The identification of at-risk students has become increasingly more significant as these students are in the precarious position of failing their courses. This study aims to achieve the objective of proposing a student performance prediction model to identify the stage of the course where at-risk students (students with the highest potential of failing their courses) can be identified based on student information system and learning management system data. The proposed student performance prediction model leverages machine learning methods to predict at-risk students, combining data from Universiti Putra Malaysia’s (UPM) Student Information System (SIS) and learning management system (PutraBlast). Two experiments were conducted to satisfy the objective. The first experiment uses the full semester data to test multiple machine learning models to identify the best model for this dataset. In the second experiment, the dataset was separated into four course stages with four predictive models trained on each stage. Students. Results show that GB outperforms other classifiers when trained on the full semester data. However, classifier performance decreases when trained on data from earlier stages of the course. Hence, based on these results, the earliest stage to predict at-risk students is identified to be the W1—W12 stage.
Electrical Muscle Rehabilitation Apparatus Using a Optocoupler and Mega-Microcontroller
Authors: Sinan S. Mohammed Sheet Et al.
Published: January 2024

Muscle atrophy is a prevalent issue observed in individuals who experience immobility or restricted mobility as a result of accidents or illnesses. Physical therapy and exercise, often employed interventions for muscle atrophy, may occasionally lack applicability. The utilization of muscle electrical stimulation has demonstrated potential as a viable therapeutic option; however, further investigation is important to optimize its effectiveness and ensure its safety.  This study investigates the possible advantages of implementing electrical muscle stimulation (EMS) and explores the utilization of a transformer, optocouplers, and MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) to enhance the efficacy of a high-capacity microcontroller-based EMS device. The issue of malfunctioning power transistors was successfully addressed through the implementation of an optocoupler-MOSFET circuit as a replacement for the power transistor. The findings of this investigation indicate that a direct electrical linkage between the signal-generating circuit and the transformer input is absent. The aforementioned features serve to ensure the safeguarding of the microcontroller circuit, establish electrical isolation between different stages of the system, and enhance the overall reliability of the EMS-designed system. The proposed framework introduces a novel technique that demonstrates a deliberate and considered approach when compared to existing studies in the field.
Natural Language Processing for Prediction of Election Results on Twitter Engagement and Polls
Authors: Tilottama Goswami Et al.
Published: January 2024

With the ability to predict political outcomes and provide insights into public opinion, using Twitter data to predict election results has gained popularity. Twitter offers a massive supply of data for analysis due to its enormous user base and real-time nature. Researchers use sentiment analysis tools to categorize tweets as good, harmful, or neutral and follow sentiment patterns over time. Network analysis finds influential users and digs deeper into the dynamics of political discourse. The accuracy of predictions is improved by combining traditional polling data with machine learning methods. Twitter data analysis has the potential to offer insightful information for election campaigns and improve political strategies despite issues like representativeness and identifying genuine sentiment. Ongoing research focuses on refining methodologies and addressing limitations, advancing the reliability of election prediction using Twitter data. The paper shows the results of election prediction for Indian political parties based on Twitter data
Protein Secondary Structure Prediction and Perceptions of Complexities using Deep Neural Network
Authors: Subhendu Bhusan Rout Et al.
Published: January 2024

The Protein molecule is known as the large biological molecule in a living organism. The protein performs several works like transporting molecules, catalysing metabolic reaction, responding to stimuli etc in a human body. Protein Structure analysis and prediction is very much essential to make any research about the same protein molecule. The basic intention of protein structure prediction (PSP) is to predict the three dimensional structure that generate by the amino acid sequence. The very peculiar matter is only twenty amino acid found in a living body where as approximately one lakh protein molecules can be framed from the same amino acid compositions in different percentages.  The three dimensional structure framed by the amino acid compositions generally changes its shape and size due to the effect of external agents or medicines that comes in contact with these protein molecules. The basic intention behind the prediction of structure of the protein is to design new drugs or medicines. From the structures the medicine researchers working for the development of medicines may easily detect the changes in the living body or the requirement of drugs or medicines. The detection of the structure and the prediction of perfect structure is always a challenging task. The protein structure is basically a three dimensional structure in its secondary transformation. The structure may be in the form of ? Helix, ? sheets or loop etc. In this paper the identification of the secondary structures and the percentages of ? Helix, ? sheets or loop structures are being predicted and the probable complexities that may occur during the prediction is discussed. Deep neural network is a deep structured learning process is an application of the broader family machine learning. Deep learning architectures has a number application in various fields like medical science, bioinformatics, medical image analysis etc. A novel method is being proposed in this research article for the detection, correction and removal of various complexities during prediction using deep neural network. This technique will be helpful for different researchers working in the field for drug design and medicine research.
Empirical Analysis of Electron Beam Lithography Optimization Models from a Pragmatic Perspective
Authors: Vinay H. Keswani Et al.
Published: January 2024

Electron Beam (EB) lithography is a process of focussing electron beams on silicon wafers to design different integrated circuits (ICs). It uses an electron gun, a blanking electrode, multiple electron lenses, a deflection electrode, and control circuits for each of these components. But the lithography process causes critical dimension overshoots, which reduces quality of the underlying ICs. This is caused due to increase in beam currents, frequent electron flashes, and reducing re-exposure of chip areas. Thus, to overcome these issues, researchers have proposed a wide variety of optimization models, each of which vary in terms of their qualitative & quantitative performance. These models also vary in terms of their internal operating characteristics, which causes ambiguity in identification of optimum models for application-specific use cases. To reduce this ambiguity, a discussion about application-specific nuances, functional advantages, deployment-specific limitations, and contextual future research scopes is discussed in this text. Based on this discussion, it was observed that bioinspired models outperform linear modelling techniques, which makes them highly useful for real-time deployments. These models aim at stochastically evaluation of optimum electron beam configurations, which improves wafer’s quality & speed of imprinting when compared with other models. To further facilitate selection of these models, this text compares them in terms of their accuracy, throughput, critical dimensions, deployment cost & computational complexity metrics. Based on this discussion, researchers will be able to identify optimum models for their performance-specific use cases. This text also proposes evaluation of a novel EB Lithography Optimization Metric (EBLOM), which combines multiple performance parameters for estimation of true model performance under real-time scenarios. Based on this metric, researchers will be able to identify models that can perform optimally with higher performance under performance-specific constraints.
Cross Modality Feature Fusion Framework for Diabetic Retinopathy Image Classification
Authors: Preeti .Rohit. Deshmukh Et al.
Published: January 2024

Diabetic retinopathy (DR) is a prevalent ocular condition and a prominent contributor to vision impairment in individuals with diabetes. Consistent monitoring through fundus photography and prompt intervention represents the most efficient strategy for controlling this ailment. Given the substantial diabetic patient population and their extensive screening needs, there is a growing inclination towards harnessing computer-aided and entirely automated methods for diagnosing DR. Over the past years, deep neural networks have achieved remarkable progress across a wide range of applications. Consequently, automating the diagnosis of DR and delivering tailored recommendations to DR patients underscores the significance of accurate and intricate DR classification. In this work, we have present a cross modality feature fusion based framework for diabetic retinopathy (DR) image classification. Here, cross modality means RGB image and it’s green channel. Initially, we have present multi-scale multi- receptive feature extraction block to learn the local and global features from both the modalities. Further, the learned features at various scale are fused effectively with present multi-level feature fusion block for image classification task. We evaluated our present framework on MESSIDOR and IDRID database by comparing it to state-of-the-art (SOTA) deep learning frame- works for DR image classification. The result analysis clearly demonstrate that the present cross-modality feature fusion based classification framework outperforms existing SOTA frameworks in terms of various evaluation parameters.
Topological Data Analysis for Software Test Cases Generation
Authors: Mani Padmanabhan Et al.
Published: January 2024

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare  expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.
Enhancing Multi-Object Detection in Video Content: Exploring Hybrid Techniques and Method Combinations for Improved Classification Accuracy
Authors: Rajkumar Govindarajan Et al.
Published: January 2024

The classification of objects within video content holds significant importance, particularly in the context of automated visual surveillance systems. Object classification refers to the procedure of categorizing objects into predefined and semantically meaningful groups based on their features. While humans find object classification in videos to be straightforward, machines face complexity and challenges in this task due to various factors like object size, occlusion, scaling, lighting conditions, and more. Consequently, the demand for analyzing video sequences has spurred the development of various techniques for object classification. This paper proposes hybrid techniques for multi object detection. The experimental analysis focused on a vehicles-openimages dataset containing 627 different catagories of vehicles. The results emphasize the profound impact of method combinations on image classification accuracy. Two primary methods, wavelet transformation and Principal Component Analysis (PCA), were employed alongside Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The evaluation encompassed performance metrics, including accuracy, precision, recall, specificity, and F1 score. In the analysis, Wavelet + RNN" combination consistently achieved the highest accuracy across all performance metrics, including accuracy percentage (96.76%), precision (96.76%), recall (86.32%), F1 score (87.12%), and specificity (87.43%). In addition, the hybrid classifiers were subjected for image classification of different vehicle catagories. In the analysis of different catagories, Wavelet + RNN" emerges as the standout performer, consistently achieving high accuracy percentages across all object categories, ranging from 82.87% for identifying People to 90.12% for recognizing Trucks.
Compact-size and Broadband Microstrip Patch U slot Antennas for C band Application and Stable Radiation Pattern overall band
Authors: Rajeev Kumar Thakur Et al.
Published: January 2024

A wide band antenna using U slot and truncated ground plane antenna is presented, The antenna used four U slot at the reflecting patch and via hole is shorted in between radiating and reflecting patch of an antenna, the antenna is all four corner truncated and four U shape is printed on ground plane. The proposed antenna is suitable for C band applications and stable radiation pattern to overall band. The antenna is resonance at a band of 7.1 to 14  GHz. The VSWR bandwidth of the proposed antenna is 65.40% and achieved a directivity of 6 dBi and gain 3.9 dBi. The size of the of the antenna is 18.18x15.541x1.5 mm3. The antenna is suitable for Wimax, WLAN and other C band applications.
Enhancing Performance of Deep Learning Models for Epilepsy Seizure Detection
Authors: Riyazulla Rahman J Et al.
Published: January 2024

Epilepsy is a neurological condition marked by recurring seizures, leading to notable effects on the well-being of individuals experiencing it. Deep learning models have shown promising results in detecting and classifying epilepsy based on electroencephalogram (EEG) data and Magnetic Resonance imaging (MRI). However, achieving high performance in epilepsy detection requires continuous efforts to enhance the accuracy and reliability of these models. This study introduces multiple approaches for improving the effectiveness of deep learning models designed for detecting epilepsy. Initially, we employ data preprocessing methods to cleanse and prepare the input data, including noise removal, data normalization, and handling missing values. Additionally, data augmentation methods, such as random rotations, translations, and scaling are employed to increase the diversity and generalizability of the training data. Secondly, various model architectures are explored to improve the model's ability to detect epilepsy. CNNs and RNNs are commonly employed, and their configurations are experimented with by adjusting the depth, and width, and adding additional layers such as residual connections or attention mechanisms. Furthermore, hyperparameter tuning techniques are employed to enhance the deep learning model's efficiency. Thoughtful choices are made regarding hyperparameters like learning rate, batch size, and regularization methods and are carefully selected through approaches like grid search or random exploration conducted to discover the best possible setup that maximizes the model's effectiveness. By implementing these strategies, the performance of deep learning models for epilepsy detection has been significantly enhanced. The improved accuracy and reliability of these models offer great potential for early detection and intervention, leading to better management and treatment outcomes for individuals living with epilepsy.
Employee Competencies Relationship Model in Hospitality: A Post COVID-19 Study Employee Competencies Relationship Model
Authors: Himani Arora Et al.
Published: January 2024

Orientation: Employee competencies are skills and behaviours laid down for an employee to meet the expectations of an organization. The paper focused on Employee competencies should be understand in a way that can be benefitted for the hospitality industry. The present study aims to analyze the employee competencies relationship model. Cases from various continents have studied how employee competencies played an important role in the hospitality sector during and after the COVID pandemic. The data was collected through structured interviews of 24 job profiles in the Hospitality sector frontline employees. MAXQDA software tool is used for code development and model testing. The study's findings infer that luxury hotel employees' self-competency and team competency are the most significant competencies. At the same time, knowledge competency presents a non-significant relationship with other competencies. Practical/managerial implications: This research focuses on specific competencies of customer-facing employees during COVID-19 service stress and the factors important for individual and team productivity.Meeting KPIs (key performance indicators) for achieving consistency in the luxury brand promise in luxury hospitality.
RSU based Joint Congestion-Intrusion Detection System in Vanets Using Deep Learning Technique
Authors: Sunanthini.J Et al.
Published: January 2024

Vehicular Ad hoc Network (VANET) is a technology that makes it possible to provide many practical services in intelligent transportation systems, but it is also susceptible to several intrusion threats. Through the identification of unusual network behavior, intrusion detection systems (ID Ss) can reduce security vulnerabilities. However, rather than detecting anomalous network behaviors throughout the whole VANET, current IDS systems are only able to do so for local sub-networks. Hence there is a need for a Joint Congestion and Intrusion Detection System (JCIDS). We designed an JCICS model that can collect network data cooperatively from vehicles and Roadside Units (RSUs).This paper, proposes a new deep learning model to improve the performance of JCIDS by using k-means and a posterior detection based on coresets to improve the detection accuracy and eliminate the redundant messages. The efficacy of the current Recurrent Neural Network (RNN) and Honey badger Algorithm (HBA)on the fundamental AODV protocol is combined with the advantages of the JCIDS is suggested in this protocol. First, formation of clusters using vehicle’s mobility parameters like, velocity and distance to enhance route stability. Moreover, a vehicle will be chosen as Cluster Head with highest route stability. Second, the efficient intrusion detection is achieved with the consumption using RNN method. In the RNN, the optimal weighting factor is selected with the help of HBA. The RNN is performing efficient prediction with the assistance of HBA. The finest path for data dissemination is selected by choosing link lifetime, hop count and residual energy along the path.As a result, multimedia data streaming is improved network life time, in terms of reduced packet loss ratio and energy consumption as compared to existing DNN and SVM scheme for different node density and speed.
Efficiency Predictor: Predicting the Consumption Efficiency of Humans by Machine Learning Technique
Authors: S. Vidya Et al.
Published: January 2024

As computer science advances and integrates with statistics in the field of machine learning, the predictability of future events is increasing. Our project focuses on leveraging this domain to forecast human performance using a minimal set of attributes, thereby reducing the need for extensive labels. As present solutions in machine learning helped humanity to predict natural events there is no accurate existing solution to predict the same for human beings. Human efficiency may include the development of an individual or the development of a team or collaboration. Making progress in a work without knowing the success rate might be a challenge as the final output may or may not give the expected results. The amount of hard work engaged in work that may fail in the future causes a great loss of time and energy. The involvement of computers integrated with the statistical models motivates and helps to predict the final output. So, we have taken the initiative to predict the future performance of a person in a more accurate and precise manner. This project aims to predict the consumption efficiency performance of a person using a machine learning algorithm by Ensemble-based Progressive Prediction.
Adaptive One-Dimensional Convolutional Neural Network for Tabular Data
Authors: Pavitha N Et al.
Published: January 2024

This study introduces an innovative approach for tackling the credit risk prediction problem using an Adaptive One-Dimensional Convolutional Neural Network (1D CNN). The proposed methodology is designed for one-dimensional data, such as tabular data, through a combination of feed-forward and back-propagation phases. During the feed-forward phase, neuron outputs are computed by applying convolution operations to previous layer outputs, along with bias terms and activation functions. The subsequent back-propagation phase updates weights and biases to minimize prediction errors. A custom weight initialization algorithm tailored to Leaky ReLU activation is employed to enhance model adaptability. The core of the proposed algorithm lies in its ability to process each training data sample across layers, optimizing weights and biases to achieve accurate predictions. Comprehensive evaluations are conducted on various machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, ensemble methods, and neural networks. The proposed Adaptive 1D CNN emerges as the top performer, consistently surpassing other methods in precision, recall, F1-score, and accuracy. This success is attributed to its specialized weight initialization, effective back-propagation, and integration of 1D convolutional layers.
Precision in Classification: A Comparative Study of Logistic Regression, Naive Bayes, LSTM, and CNN for Spam Email Detection
Authors: Ratnam Dodda Et al.
Published: January 2024

This research presents a comprehensive comparative study on the precision of classification algorithms applied to spam email detection. The study focuses on four distinct algorithms: Logistic Regression, Naive Bayes, Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). The evaluation encompasses a common dataset to ensure a fair and rigorous assessment of each algorithm’s performance. We investigate and analyze precision metrics, considering the trade- offs between true positives and false positives. The comparative study provides insights into the strengths and limitations of each algorithm in effectively identifying spam emails. The results contribute valuable knowledge to the field of email security and classification, guiding practitioners and researchers towards informed algorithm selection based on precision considerations.
The Effect of Crop Growth Stage on the Formation And Ability of Carbon Occlusion within Phytoliths in Rice Varieties
Authors: R. Vidhya Et al.
Published: January 2024

Carbon occlusion refers to the process by which carbon dioxide is incorporated into the silica structure of phytoliths during their formation. Microscopic silica formations called phytoliths develop inside plant cells. Phytoliths are microscopic, silica-based structures that develop inside plant cells. These silica bodies are deposited in various parts of the plant, such as leaves, stems, and reproductive organs. Carbon occlusion within phytoliths can potentially act as a long-term carbon sink, aiding in the mitigation of greenhouse gas emissions and climate change. They are commonly found in various plant species, including rice. This study focuses on investigating how the growth stage of rice crops affects the process of carbon occlusion within phytoliths. Phytoliths play important roles in plant structure, defense mechanisms, and carbon storage. The research study aims to examine how different growth stages of rice crops influence the formation of phytoliths and the extent to which carbon is occluded within them. It may involve conducting experiments or observations at different growth stages of rice plants, such as the seedling stage, vegetative stage, flowering stage, or maturity stage. The findings of this research could contribute to our understanding of the carbon sequestration potential of rice crops and how it varies throughout their growth cycle.
Exploring Diverse Rough Neighborhoods Through Graphical Analysis
Authors: Aruna Devi. R Et al.
Published: January 2024

The world’s knowledge is believed to double every ten months, and this vast pool of information often contains incomplete data, imperfections, uncertainties, and vague elements. Converting such data into meaningful patterns is a crucial task for data analysts. In this research, we have explored various mathematical models for this purpose. Among these models, we focused on Pawlak’s Rough Set model applied through Rough Graphs. Our work presents a novel form of Rough Neighborhood System, as demonstrated in this paper.
Digital Transformation and Smart Property Contracts Registration in Egypt Using Blockchain Technology
Authors: Mohamed Saleh Et al.
Published: January 2024

In recent times, an increasing fascination with cryptographic-based blockchain technology has emerged. Blockchain is a decentralized, distributed database functioning as a comprehensive ledger for storing, timestamping, and encrypting data. Comprising interconnected blocks, this technology is openly accessible and immutable. Smart Property Contract registration are self-executing contracts that are subject to the terms of the agreement between the seller and the buyer. The contract defines the pre-programmed contractual rules so that they are executed when the input data meets the mentioned conditions between the two parties. Smart Property Contract Registration are written in one of the programming languages such as AssemblyScript, JavaScript, and Solidity. Blockchain technology and Smart Property Contract Registration can play a vital role in Property and smart cities in the future. The core aim of this thesis is to design a proposed model for registering Smart Property Contract Registration in Egyptian Egyptian Property Registry Office and solve the traditional registration problem of slow registration and high cost and insecure. The proposed model was proposed and implemented on the NEAR platform. Egyptian Property registration system was built using programming code for Smart Property Contract Registration written in a high-level language such as AssemblyScript and HTML, JS, and React Js programming code, and also present research uses a questionnaire study to ensure the validity of the proposed model. We conducted a questionnaire by asking a set of questions to those interested in the subject of blockchain and Smart Property Contract Registrations. The result showed that the adoption of blockchain technology in Egypt will be beneficial in terms of easy access to Property transactions in a quick time, reliability, and cost reduction.
Automated Detection of Autism Spectrum Disorder Using Bio-Inspired Swarm Intelligence Based Feature Selection and Classification Techniques
Authors: S.Saravana Kumar Et al.
Published: January 2024

Autism spectrum disorders, or ASDs, are neurological conditions that affect humans. ASDs typically come with sensory issues like sensitivity to touch or soundor odour. Though genetics are the main causes, their  early discovery and treatments are imperative. In recent years, intelligent diagnosis using MLTs (Machine Learning Techniques) have been developed to support conventional clinical methods in the domain of healthcare. Feature selections from healthcare databases consume nondeterministic polynomial timesand are hard tasks where again MLTs have been of great use. AGWOs (Adaptive Grey Wolf Optimizations) were used in this study to determine most significant features and efficient classification strategies in datasets of ASDs. Initially,  pre-processing strategies based on SMOTEs (Synthetic Minority Oversampling Techniques) removed extraneous data from ASD datasets and subsequently AGWOs  repeat this procedure to find smallest features with maximum classifications values for recall and accuracy. Finally, KVSMs (Kernel Support Vector Machines) classify instances of ASDs from the input datasets. The experimental results of suggested method are evaluated for classifying ASDs from datasets instances of Toddlers, Children, Adolescents, and Adults in terms of recalls, precisions, F-measures, and classification errors.
Data Sharing based on Facial Recognition Clusters
Authors: Rajkumar Rajasekaran Et al.
Published: January 2024

The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a database. Our strategy capitalizes on the dlib library and its face recognition model, which com- bines advanced deep learning methods with traditional computer vision techniques to attain highly accurate face detection and recognition. The essential elements of our system encompass face detection, face recognition, and image retrieval. Initially, we employ the face recognition model to detect and pinpoint faces within the captured image. Following that, we employ facial landmarks and feature embeddings to recognize and match the detected face with entries in a database. Finally, we retrieve and present all images connected to the recognized individual. To validate the effectiveness of our system, we conducted extensive experiments on a diverse dataset that encompasses various lighting conditions, poses, and facial expressions. Our findings demonstrate exceptional accuracy and efficiency in both face detection and recognition, rendering our approach suitable for real-world applications. We envision a broad spectrum of potential applications for our system, including access control, event management, and personal media organization.
SM-DBERT: A Novel Symptom-based Technique for Chronic Disease Classification using DISTILBERT
Authors: Swati Saigaonkar Et al.
Published: January 2024

Machine learning and deep learning models when applied on EHR systems are considerably augmenting the prediction tasks performed on medical data. Humongous amount of information lies in the free form clinical texts. But there exist challenges associated with such kinds of unstructured data. Transformers based models like Bidirectional Encoder Representations from Transformers (BERT) has revolutionized the work. DISTILBERT, a lighter version of BERT, is even promising as the time required is reduced to nearly one-third without losing the performance. In this research work, we present SM-DBERT, Symptom-based Modified DistilBERT architecture designed for Chronic Diseases. The foundation of SM-DBERT is symptomatology, as an optimal model should prioritize symptoms as they are the key indicators. The existing DISTILBERT architecture has been modified by introducing additional layers and extra embeddings of external knowledge and presented along with input ids and attention masks. These extra knowledge helps the model to learn more relevant information.  SM-DBERT has demonstrated notable improvement in the results. The accuracy obtained with this novel approach is 0.98 as against the basic DISTILBERT model.
A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques
Authors: S Nagarajan Penugonda Seetha Rama Krishna,
Published: January 2024

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity.
Neural Machine Translation from Bengali Language to English language and vice-versa
Authors: Arindam Roy Et al.
Published: January 2024

Bengali ranks among the first ten spoken languages in the world with a native speaker numbering about 230 million people.  With UNESCO declaring 21st February as International Mother Language Day to commemorate the laying down of lives by five Bangladeshi students for the cause of their mother tongue, Bengali has come into the radar of worldwide  attention . Though significant amount of prose, poetry have been written in Bengali language and large number of newspapers in Bengali get published daily, technically it is still considered a Low Resource Language (LRL) unlike English or French which are High Resource Language (HRL). The reason is not far to seek as corpora in varied domains such as short stories, sports, politics, agriculture etc is less in number and even when they are available, the size is less. Machine translation (MT) is difficult to perform in Bengali as parallel corpora from Bengali to other languages and vice versa is few and far between and when they are available they suffer from the problems of size and quality. This work is aimed at implementing one state of the art model in Neural Machine Translation (NMT) which is called the self-attention transformer model to perform translation from English to Bengali and vice versa. Though a couple of research work has been published in the recent years on MT from English to Bengali, they are mostly domain specific. This paper does not focus on any specific domain for NMT from English to Bengali and as such may be conceived as a more of general domain NMT from English to Bengali which is more difficult than domain specific NMT. Performance evaluation of the model was done  using BLEU version-4  vis-à-vis translations of well known English-Bengali MTsystems.
DDoS Mitigation by Blockchain With Approach of Cost Model
Authors: Laxmi Poonia, Et al.
Published: January 2024

Computer networks and internet services are increasingly threatened by attacks like Distributed Denial-of-Service (DDoS). DDoS attack mitigation techniques now in use are ineffective due to a lack of resources and a lack of adaptability. Using blockchains like Ethereum, DDoS attacks can be thwarted in innovative ways. With smart contracts, it is possible to track down the IP addresses of attackers without additional hardware. This study examines blockchain-based solutions to combat DDoS attacks for feasibility, effectiveness, as well as cost and performance. The cost model delves into economic aspects like gas, gas price, and Ether value. In it, the evaluation of various smart contracts for the signalization of DDoS attacks is documented and compared to assess three system variants, analyzing gas costs, deployment, speed, and accuracy. It also details Ethereum's ecosystem and how that affects smart contract design and it also acknowledges scalability challenges and suggests outsourcing data for a more scalable solution, advocating for specialized blockchains for DDoS signaling applications. The analysis provides insights into the gas costs associated with different variants, considering various scenarios and highlighting the trade-offs and efficiencies of each approach.
A Critical Implementation Strategy of Various Diseases of Banana Plant based on Deficiencies of Minerals in the Soil
Authors: Chukka Keerthana, Et al.
Published: January 2024

The period now occurring era, the agriculture scenarios generally contribute to 6.4% of the entire world's economic production. At least nine nations around the world have agriculture as their primary economic sector. As well giving billions of people access to fuel, agriculture also gives a lot of people job opportunities. This paper mainly focusses on identifying diseases of banana plant based on the deficiencies of minerals in the soil. First, we gathered the information about diverse categories of diseases occurs in banana plant such as Panama wilt, Mycosphaerella leaf spots, yellow sigatoka, black sigatoka etc… Also collected details of minerals in the soil. And categorized them into macro nutrients and micronutrients. We made the comparative analysis of minerals based on the types of soils. In this paper, we aim to researched about the diseases of banana plant.
An Overview of the Different Kinds of Vector Space Partitions
Authors: K. Anuradha Et al.
Published: January 2024

In a finite vector space V (n,q), where V is n-dimensional over a finite field with q elements, a collection P of subspaces is called a vector space partition. The property of this set P is that any vector that is not zero may be found in exactly one element of P. Partitions of vector spaces have strong ties to design theory, error-correcting algorithms, and finite projective planes.

The first portion of my talk will focus on the mathematical fields that share common ground with vector space partitions. The rest of the lecture will go over some of the most well-known results on vector space partition classification. Heden and Lehmann's result on vector space partitions and maximal partial spreads, as well as El-Zanati et al.'s recent findings on the types found in spaces V(n, 2) for n = 8 or less, the Beutelspacher and Heden theorem on T-partitions, and their newly established condition for the existence of a vector space partition will all be covered. Furthermore, I will demonstrate Heden's theorem about the tail length of a vector space split. Finally, I shall provide some historical notes.
Handling Imbalanced Classes for Model Training in Fake News Detection
Authors: Ashwini Deshmukh Et al.
Published: January 2024

With the widespread dissemination of news on social media platforms, the propagation of fake news has become a pressing concern. Detecting fake news is crucial to maintaining the integrity of information shared across social networks. This paper presents a comprehensive investigation into the detection of fake news on social media, focusing on the collection of data from both reliable and unreliable sources To build an effective fake news detection system, a diverse dataset encompassing both reliable and unreliable sources is collected.  This data collection strategy ensures a comprehensive representation of the information landscape present on social media platforms.The implementation of the bidirectional LSTM with an attention layer is a powerful approach that has shown promising results in various natural language processing tasks, including text classification and sentiment analysis. Its effectiveness lies in its ability to leverage both directional information and attention-driven focus, allowing the model to better understand and interpret the nuances of the input sequence. Calculate the class weights using the inverse of the importance factors to give proper weight to each label. Balancing has been tried with the class weight method the system has given almost 60% accuracy.
Recognizing Criminal Intent through Facial Expressions
Authors: Gnanaprakasam C Et al.
Published: January 2024

Artificial intelligence is developing rapidly as a result of recent advances in the identification of image and emotion patterns in human facial features. On the other hand, the rise in crime and wrongful imprisonment are causing society to disintegrate. Artificial intelligence has greatly aided in the development of our contemporary society. Knowing the issue and the appropriate set of instruments to address it is an essential skill, and having the ability to apply those tools effectively elevates one to the rank of supreme being in the cosmos. False imprisonment is an issue that requires attention. Recognizing the facts,1 in 20 criminal prosecutions in the US alone end in an incorrect conviction. Innocent persons who have been unfairly convicted make up 1% of US jail populations, or about 20,000 people, according to the Innocent Project group. Facial emotion detection and datasets gathered based on the study "Criminality in the face" can be used by artificial intelligence to help lessen the problem of false convictions. According to a Kinesics survey, specific body motions and movements can be used as a kind of non-verbal communication. By Ray Birdwhistell in 1952, the phrase was first used. As the proverb "Face is the index of mind" states, facial expressions are an important aspect of non-verbal communication. Analyzing a person's face might provide insight into the circumstances around a criminal suspect.
Proposal For 6LoWPAN Wireless Network Protocol-Based Street-Light-As-A-Service (SLaaS) Framework To Power Campus Parking Services
Authors: Lokman Mohd Fadzil Et al.
Published: January 2024

A novel IPv6 Over Low Power Wireless Personal Area Network (6LoWPAN) protocol-based Street-Light-As-A-Service (SLaaS) research framework for an integrated cloud-based smart university campus parking platform is being proposed. The Intelligent Connected Street Light infrastructure currently in existence at University Sains Malaysia (USM) is being redesigned. As part of overall parking proposal, approaching object image and video data are being acquired using a range of sensors, including the  passive infrared (PIR) and 3-D Light Detection and Ranging (LIDAR) motion sensors. To acquire and transmit vehicle instrumentation data, ELM327 Onboard Diagnostics 2 (OBD-II) Wi-Fi adapter is being used. For edge computing-based intelligent object detection model processing to synchronize license plate and facial recognition data with USM databases, Nvidia® Jetson Nano is being used. The framework is developed to effect multiple services, including to track and manage university vehicles, to detect and secure pedestrian movement, to trigger lighting commands based on approaching vehicle movement for on-demand parking illumination, to partition digital bounding box perimeter to predict potential vehicle collisions, to detect unregistered and excessive parking time to monitor unattended and illegal vehicles, to enable street-light-located parking-related kiosk services, and consolidate cloud-based human and vehicle data analytics into mobile form-factor dashboards. The proposed design will be evaluated and validated for end-to-end average data propagation rate, data integrity, object-to-human and object-to-vehicle synchronization success rate, electrical power consumption reduction and potential surface attack mitigation and avoidance throughout the network.