bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Analysis of Routing Protocol Using Trust-Based Hybrid FCRO-AEPO Optimization Techniques
Authors: S. Mohan Et al.
Published: January 2024

Mobile Ad hoc Networks (MANETs) offer numerous benefits and have been used in different applications. MANETs are dynamic peer-to-peer networks that use multi-hop data transfer without the need for-existing infrastructure. Due to their nature, for secure communication of mobile nodes, they need unique security requirements in MANET. In this work, a Hybrid Firefly Cyclic Rider Optimization (FCRO) algorithm is proposed for Cluster Head (CH) selection, it efficiently selects the CH and improves the network efficiency. The Ridge Regression Classification algorithm is presented in this work to sense the malicious nodes in the network and the data is transmitted using trusted Mobile nodes for the QoS enactment metric improvement. A trust-based routing protocol (TBRP) is introduced utilizing the Atom Emperor Penguin Optimization (AEPO) algorithm, it identifies the best-forwarded path to moderate the routing overhead problem in MANET. The planned method is implemented using Matlab software and the presentation metrics are packet delivers ratio, packet loss ratio (PLR), routing overhead, throughput, end-to-end delay (E2ED), transmission delay, energy consumption and network lifetime. The suggested AEPO algorithm is compared with the prevailing PSO-GA, TID-CMGR, and MFFA. The AEPO algorithm’s performance is approximately 1.5%, 3.2%, 2%, 3%, and 4% higher than the existing methods for PLR, packet delivers ratio, throughput, and E2ED and network lifetime. The sender nodes can increase their information transmission rates and reduce delays in appreciation of this evaluation. Additionally, the suggested technique has a perfect benefit in terms of demonstrating the genuine contribution of distinct nodes to trust evaluation (TE).
A Comprehensive Survey on Internet of Things for Smart Cities : Applications, Communication Protocols, Network Types and Requirements
Authors: Azmera Chandu Naik Et al.
Published: January 2024

Rapid population growth in recent decades has made it imperative to find innovative answers to pressing problems in areas like transportation, healthcare, energy, and public works. One of the most interesting technology enablers for meeting these difficulties is the Internet of Things (IoT), which involves the global interconnection of a vast quantity of items equipped with electronics, software, sensors, and network connections. The IoT is a cutting-edge contemporary technology that has the potential to link a wide variety of digital objects to the web, enabling a wide variety of new services within the context of a "smart city". These gadgets come equipped with a wide range of sensing, acting, and processing options. Attractive IoT technology and big data analytics are making smart city plans a reality all around the world. By improving infrastructure and transportation networks, decreasing traffic congestion, managing trash, and enhancing the quality of human life, these services are revolutionizing urban areas. When fully implemented, the IoT will allow for the seamless integration of a wide range of disparate end systems and the open sharing of data for the development of an abundance of digital services. The focus of this essay is on an urban IoT framework, a still-broad category that is yet to be characterized by its particular application domain. Actually, urban IoTs are made to support the Smart municipal concept, which seeks to apply current communication technology to supply extra services to municipal administration and people. This article, therefore, offers a comprehensive analysis of the relevant technologies, protocols, and architecture.
Identification and Diagnosis of Breast Cancer At Different Stages By Different Machine Learning Algorithms On The Coimbra Dataset
Authors: Manish Tiwari Et al.
Published: January 2024

Cancer is the most deadly disease in the world. Breast cancer is the second-most common disease in women worldwide. It is the most common cancer globally among women. Annually, 12.5% of all new cancer cases worldwide Globally, 2.26 million breast cancers were discovered, and 685,000 women died from this disease. Early diagnosis of breast cancer is more difficult in developing countries than in developed countries. Using technology, if it is possible to detect cancer early and treat it on time, then many women can be cured and their lives can be saved. Early detection also leads to an increased survival rate for patients who receive clinical therapy before reaching later stages. It includes a number of risk factors, such as modifiable and non-modifiable ones. A recent survey discovered that for women above 50 years of age, the chance of getting breast cancer is about 80%. Machine learning algorithms are playing a major role in diagnosing liver cancer in its early stages and helping doctors make prompt decisions. A number of machine learning models have been executed in which the model gave better performance in terms of accuracy, and other parameters such as precision, recall, etc. are used to predict early. In this research work, the latest dataset, Coimbra, belongs to UCI machinery. It has nine features (age, BMI, glucose, insulin, HOMA, leptin, adiponectin, Resistin, MCP.1) and one classification attribute, which has values 1 and 2. 1 belongs to benign, and 2 belongs to malignant. Based on that, the supervised machine learning algorithm was applied. The WEKA tool is used to analyze the dataset. A number of algorithms are applied, such as Bayes net, multilayer perceptron, IBK, random committee, random tree, etc. More of them gave better results, and that model was chosen as the key model for breast cancer analysis.
Analysis and Prediction of Winning Team and Player Performance in Franchise Cricket using Machine Learning
Authors: Tumma Susmitha Et al.
Published: January 2024

Sports, especially cricket, produces an enormous amount of statistical data that has led to the evolution of the game. Unlike other sports, cricket involves numerous variables ranging from pitch conditions, weather, boundary length, and several other factors that make each game unique. These variables make every match have its own significance, making cricket an ever-evolving sport that continues to captivate its audience. Over the years, cricket has witnessed a massive transformation with the integration of analytics. The use of data analytics has changed the way players and coaches approach the game. It has brought a new dimension to the game and has opened several new avenues for analyzing and interpreting the game. The Indian Premier League (IPL) has acted as a catalyst in showcasing the potential of cricket to the world and has bridged the gap between different cultures and audiences. In the present-day IPL, every statistic available is being used due to the high level of competitiveness of the tournament. Teams use data analytics to understand their strengths and weaknesses, analyze their opponents' gameplay, and devise strategies to win matches. These analytics provide valuable insights into various aspects of the game, such as player performance, team strategies, and game trends, among others. This paper is a sincere effort to uncover hidden insights in IPL by utilizing data from previous seasons. The vast amount of data available offers a treasure of formation that can help us to understand the nuances of the game and make informed decisions. By analyzing this data, we can identify patterns and trends, assess player performance, and make informed decisions that can impact the outcome of a match.
Heuristic Approach For Weighted Two Stage Flow Shop Model In A String Of Disjoint Job Block Under No Idle Constraint
Authors: Harshleen Kaur Et al.
Published: January 2024

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which jobs are scheduled to run in a string of two job blocks that are disjoint in nature, and the jobs processing time is correlated with probabilities. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. In order to eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to efficacy of the proposed strategy operates. The outcomes are contrasted with the current methods that Johnson and Palmer have demonstrated.
Status and problem of Integrated Library Management system among Engineering college Libraries in TamilNadu with Special Reference to Kancheepuram district
Authors: J.Jaganbabu Et al.
Published: January 2024

Libraries are becoming more and more dependent on information and communication technology (ICT), especially with regard to automation and technological know-how. The prevailing paper is displays the reputation and troubles of Library automation in aided university Libraries affiliated with Anna College In Kancheepuram district. The check was changed to unquestionably state that the Kancheepuram district' 87.50% of libraries are automated. And basic problems that are currently not automated include a lack of staff, a lack of infrastructure, a lack of funding, and the absence of human education in libraries. A status view of the software packages that libraries and modules use is also provided by this. The availability, applicability, and issues encountered during the process of implementing and using library automation software applications are the study's specific areas of focus.
Protection Scheme based on Artificial Neural Network for Fault Detection and Classification in Low Voltage PV-Based DC Microgrid
Authors: Ramaprasanna Dalai Et al.
Published: January 2024

With the expansion of the DC distribution market, protection, and operational concerns for Direct Current (DC) Microgrids have increased. Different systems have been investigated for detecting, finding, and isolating defects utilising a variety of protective mechanisms. It might be difficult to locate high-resistance faults and shorted DC faults on low-voltage DC (LVDC) microgrids. Therefore, in this study, a Field Transform Technique like Short-Time Fourier Transform (STFT) is proposed for detecting the Fault Current (FC). This method detects the faults Pole-ground (PG), pole-pole (PP), and Arc fault are the major fault types in the DC network with PG fault as the most common and less severe. One of the difficulties the DC system faces in the incidence of a malfunction is the protection of essential converters. During this fault, the diodes, being the most vulnerable component of the system, may encounter a substantial surge in current, which can potentially cause damage if the current surpasses double their specified capacity for withstanding. After the Fault detection (FD), a Taguchi-based ANN is presented to classify the detected faults. This method effectively classifies PV-based faults. Then, to safeguard the FC, the Improved Self-Adaptive Solid State Circuit Breaker (I-SSCB) is introduced. It safeguards the FC in the low-voltage PV-based DC microgrid (DCMG) and restricts FC in the DCMG. The suggested approach is evaluated using the Matlab software and the proposed method produces 400A current and 100 KW power during the PV temperature of 25°C. The output current of the ANN is then 1A for a duration of 0.3 to 0.4 seconds. The fault voltage and FC produced in this proposed work are 1900V and 1950A. Therefore, the proposed work's current and voltage values are 21 KV and 0.35 I. Therefore, the proposed method produces more power and limits the FC in the LV-DCMG. In future studies, the improved or modified neural network or machine learning (ML)-based techniques can be utilized which may improve the protection scheme of the work.
Snowball-Miner: Integration of Deep Learning for Extraction of Cyber Threat Intelligence from Dark Web
Authors: Abir Dutta Et al.
Published: January 2024

In Cyber threat intelligence is a crucial component in defending against cybersecurity threats. Cyber security dark web, security Blogs, Hackers’ community, news forums, Open-Source Intelligence (OSINT) are known as the harbor of illicit activities and serve as a breeding ground for cybercriminals. Extracting actionable intelligence from the dark web is challenging due to its anonymous and encrypted nature. State-of-art work proposed machine learning and deep learning approach to aggregate the dark web for cyber threat intelligence from data present in the dark web.  This paper proposes, a novel approach utilizing Snowball-Miner for cyber threat intelligence discovery from the dark web. The model is trained on a diverse dataset consisting of dark web forums, hidden .onion based marketplaces and other underground platforms using Snowball-crawler. However, we have employed hybrid convolutional model CNN-LSTM and CNN-GRU adopting doc2vec word embedding to classify into four domains viz Energy Sector, Finance, Illicit Activities and illegal Services. From our experiment it emerged that, CNN-LSTM outperforms as 96.37% for classification of domain specific threat documents. Furthermore, after data preparation we implemented NLP technique and extracted the domain specific Indicator of Compromise (IoCs) using RegEx parser and Subject, Object and Verb (SOV) semantics dependency analysis. Finally, we have integrated IoCs and Threat keywords with respective domains to generate domain specific threat intelligence which enhance the quality of the domain specific CTI based on R-dimension (Relevance).
System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition
Authors: Sarabjeet Singh Et al.
Published: January 2024

Social recommender systems are getting up more attention for product advertisement and social connectivity. A good recommender               should think about the system and the user. The user will have a preference list of some items and these preferences can be useful in suggesting the things which can help the endorsing system to identify better items. In this paper, the idea of social recommender systems as a pattern matching and regular expression making is used for unification of similarities. The concept of mutual profile pattern expression can be applied on various networking platforms. In these type of shared platforms, people all around the globe share resources and interact with each other. In order to manage or scrutinize users according to their interests and likeness, the mutual profile pattern of users can be used. Further predicting of membership function is performed to show how much extent does the profile matches.
Comparative Analysis of Decision Tree (DT) & Deep Neural Network (DNN) Techniques to Diagnose the Stator Winding Fault in the Permanent Magnet Synchronous Motor (PMSM)
Authors: Prakhar Singh Bhadoria Et al.
Published: January 2024

Every industry is moving towards automation, and Electrical motors like DC and AC play a significant role in the overall automation scheme. Electrical motors, however, are prone to multiple manufacturing defects, and untimed wear and tear. Automated detection of faults, while the motor is in operations, can prevent automation breakdowns, and unforeseen accidents. The majority of faults in electrical motors are because of overheating of windings, bearing misalignment, shaft mismatch, vibrations, noise, etc. A few hours of Electric Motor running can generate a lot of sensor data measuring temperature, torque etc.. Manual analysis of such data can be extremely time consuming. Machine Learning can be used to perform automated analysis of data and real-time fault detection as suggested by many experts. In this research, we have used a few machine learning techniques to train the model on top of sensor data and use the model to diagnose the electric motor faults, occurring because of heat in the stator winding part of the permanent magnet synchronous motor (pmsm). We have specifically compared Decision Tree (DT) and Deep Neural Network (DNN) techniques on real-life datasets, and compared their accuracy. As per the final results, Decision Tree (DT) and Deep Neural Network (DNN) have 93.82% & 97% accuracy respectively.
T1- Weighted MRI Image Segmentation
Authors: Sujata Tukaram Bhairnallykar Et al.
Published: January 2024

Growing evidence in recent years indicates that interest in the development of automated image analysis techniques for medical imaging, especially with regard to the discipline of magnetic resonance imaging. T1-weighted MRI scans are often used for both diagnosis and monitoring various neurological disorders, making accurate segmentation of these images crucial for effective treatment planning. In this work, we offer a new method for T1-weighted MRI image segmentation using patch densenet, an image segmentation-specific deep learning architecture. Our method aims to improve the accuracy and efficiency of segmentation, while also addressing some of the challenges associated with traditional segmentation methods. Traditional segmentation methods typically rely on features that are handcrafted and may struggle to accurately capture the intricate details present in MRI images. By utilizing patch densenet, our method automatically learn and extract relevant features from the T1-weighted MRI images and further enhance the accuracy and specificity of the segmentation results. Ultimately, we believe that our proposed approach can greatly improve diagnosis and treatment planning process for neurological disorders.
An Empirical Performance Analysis of Multi-Classification of Diseases of Tomato Leaf using CNN Models in the Deep Learning
Authors: Sravan Kumar G Et al.
Published: January 2024

Tomato farming in India, producing tomatoes is one of the leading productions and stands second-largest producer of tomatoes in the world. Tomato farming has been facing challenges as the crop is susceptible to tomato diseases that include Bacterial_Spot, Early_Blight, Septoria_Leaf Spot, Spider_Mites and Late_Blight, that accounts to massive decline in the crop production. The significant drop in the production raises alarm in the analysis of the leaf of tomato with adoption of state of art technologies into the farming. The analysis of tomato leaf with the intent of early prediction of particular disease, includes employment of Convolutional Neural Network (CNN) Models include LetNet5, ResNet50 and AlexNet of the Deep Learning. The proposed work employed the kaggle database tomato leaf diseases dataset that contains 10,000 images that consist of healthy leaves and disease affected leaves. Deep Learning includes Convolutional Neural Networks models: LetNet5, ResNet50, AlexNet are applied on the  disease affected and healthy leaves of tomato dataset and it is performed empirical analysis of the CNN models in the prediction of diseases of leaf of tomato through metrics related to performance such as F1-Score, Accuracy, Precision, Recall. The proposed work which highlights empirical performance analysis of the CNN models: LetNet5, ResNet50, AlexNet, provided the noteworthy result that ResNet50 model is able to perform multi-classification the tomato leaf diseases with better accuracy 0.98701 and F1-score 0.98932.
Quantum Machine Learning Technique for Automatic Retrosynthetic Reaction Pathway Search Method
Authors: Jagannath Jijaba Kadam Et al.
Published: January 2024

Retrosynthetic analysis often involves evaluating many potential candidate reaction pathways and molecules at multiple stages of the reaction, resulting in complex retrosynthesis trees that need to be searched and parsed efficiently. Computational approaches could significantly aid the chemist in  solving different aspects of the retrosynthesis problem, such as the graph-theoretic search methodologies for efficient tree traversal to identify feasible reaction pathways, dictionary-based methods to evaluate a large search space of precursors, and chemistry-driven heuristics to eliminate practically infeasible routes. In this research, a new single-step retrosynthesis prediction method of the Retro TRAE SMILES-based translation technique is proposed. Accordingly, quantum computing with tree-tensor network topology is presented to construct an automatic data-driven end-to-end retrosynthetic route planning system (Auto-Syn-Route), which is presented based on the heuristic scoring function. AutoSynRoute successfully reproduced published synthesis routes for the four case products. The model is trained in an end-to-end and fully data-driven fashion. Unlike previous models translating the SMILES strings of reactants and products, a new way of representing a chemical reaction based on molecular fragments is introduced. It is demonstrated that the new approach yields better prediction results than current state-of-the-art computational methods. The new approach resolves the major drawbacks of existing retrosynthetic methods such as generating invalid SMILES strings. The proposed method is implemented using Python software. The proposed approach predicts highly similar reactant molecules with an accuracy of 68%. In addition, the proposed method yields more robust predictions than existing methods. However, the experiments demonstrate that the proposed scheme significantly improves the success rate of solving the retrosynthetic problem by 97% while maintaining the performance of the quantum tree tensor for predicting valid reactions.
A Survey on Automation Challenges and Opportunities for IoT based Agriculture
Authors: Vinay Kumar Enugala Et al.
Published: January 2024

Agriculture automation is a major concern and a contentious issue in every country. This study provides a comprehensive assessment of the obstacles and potential associated with automating agricultural practises using IoT (Internet of Things) technology. It begins with an introduction that highlights the prior work and discusses the proposed proposal, which is centred on IoT and machine learning applications and breakthroughs in irrigation systems. The report digs into several IoT applications in agriculture, including crop and soil management, drone field surveillance, cattle and resource management, and pesticide/fertilizer tracking. It delves into the breakthroughs made possible by IoT and machine learning, particularly in smart irrigation systems, livestock monitoring, drone technology, precision agriculture, and integrated pest management. The paper thoroughly examines the challenges associated with automating irrigation practises, such as interoperability, data storage, connectivity, hardware and software maintenance, security concerns, data collection, environmental variability, cost, infrastructure, privacy, and adoption by small-scale farmers. The survey finishes by synthesising the important findings and emphasising the crucial need of overcoming these problems in order to successfully adopt IoT-driven agriculture automation.
Improved Rsa Algorithm for Data Security against DDoS Attack in a Cloud-based Intrusion Detection System
Authors: S. Sujitha Et al.
Published: January 2024

Today, more and more industries are using cloud computing for some integration operations, but ensuring the security of user data and system resources remains a challenge. This article proposes a method to identify and mitigate unwanted packets and traffic, especially duplicate packets, in cloud computing environments. The method includes creating an Intrusion Search and Detection (IF-AD) system to securely maintain user information and allocate secondary memory. To detect unwanted traffic, this method compares the size of the downloaded file  with the original file, identifying any differences as potential DDoS.  RSA encryption mechanism is used for subsequent file transfers for added security. The proposed approach aims to enhance the security posture of cloud-based systems by detecting and preventing unauthorized access and file modification.
Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach
Authors: Et al. Jagannath Jijaba Kadam
Published: January 2024

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19.
Modified LMS Adaptive Algorithm for the determination of Maximum SNR using Self-Adaptation Technique of Noise Factor for Communication System
Authors: Garima Kulshreshtha Et al.
Published: January 2024

The most often used adaptive filter (AF) is the least-mean-square (LMS) filter. This filter has many applications in the area of communication and signal processing. System identification represents a significant use case for adaptive filters. This paper represents a new structure/model of system identification and adaptive noise cancellation (ANC) using state-of-the-art LMS-AF. The paper expressed the algorithm of updated LMS-AF. The major parts of the proposed model are- two adaptive filters, one LMS filter, one correlation function, and one auto-correlation function. The research investigation for the proposed model is based on the self-adaptation or self-learning method to obtain the maximum signal to noise ratio (SNR) based on the real-time inputs. The proposed structure/model converges towards the ideal LMS-ANC system. This paper also includes mathematical analysis, simulation, results, and discussion. The most important comparison parameters are the SNR and mean-square-error (MSE).
Self-Improved Pelican optimization for Task Scheduling in Edge Computing: Neural Network based risk probability estimation
Authors: Bantupalli NagaLakshmi Et al.
Published: January 2024

The aim of edge computing is to drastically speed up the task response time while using less energy. The computational resources in EC are situated in closer proximity to the information generation sources, resulting in lower network latency and bandwidth utilization related to cloud computing (CC).  In an EC system, the edge server handles and manages the requests of task and generated information from adjacent IoT machines. The task's schedule is regarded as the optimization problem. Thus, this paper aims to provide a novel task scheduling model that considers Risk probability, Execution cost, Execution time, and Makespan in to account. The Neural Network specifically estimates risk probabilities while taking task security and virtual machine security into consideration. This work suggests a new Paetro distribution-based pelican optimization algorithm (PDPOA) model for optimum scheduling of tasks. Results from the proposed system are examined and compared to existing methods via certain measures including Makespan, Execution time, Execution cost, Risk probability, etc.
Graphical Authentication System Using Image Panels
Authors: Y.Bhavani Et al.
Published: January 2024

The use of alphanumeric usernames and passwords is the most widely used technique for authentication. It is found that this technique has serious drawbacks. For instance, users frequently select passwords that are simple to remember. On the other hand, it may be challenging to recollect a complicated password. The creation of an OTP is another option, but it can take some time and comes with a risk (losing it in the allotted time). These existing methodologies have some disadvantages. A graphical authentication is the best to remember and hard to guess. So, a graphical authentication technique is proposed to address the problems of low security, shoulder surfing, dictionary and brute force attacks. In this methodology, the user must register by providing the required information and by selecting a panel from 3-5 images. This methodology is tested using entropy and proved that this approach is efficient than the existing methods..
Survey Analysis on Secured user Authentication through Biometric Recognition
Authors: Joseph Charles P Preethi S,
Published: January 2024

Secured user authentication is the process of verifying the user authenticity. Biometric authentication is the human identification system employed to match the biometric characteristics of user for verifying the authenticity. Biometric identifiers are exclusive, making it harder to hack accounts using them. Common types of biometrics comprise the fingerprint scanning verifies authentication based on a user's fingerprints Face recognition and voice recognition are employed in real-time application for improving the security level in different application scenarios. Face recognition is a method of identifying or verifying the individual identity using their face expression. Voice recognition is the ability of machine to receive and interpret the dictation to understand. Many researchers carried out their research on different face and voice recognition methods. But, recognition accuracy was not improved with minimum time consumption by existing biometric recognition method. In this research, different recognition methods are reviewed using biometric recognition method for user authentication. The recognition methods are efficiently on human faces dataset with respect to performance metrics like recognition accuracy, error rate, and recognition time.
Backpropagation Neural Network Adaptive Voltage Control of a High-Gain Transformerless DC/DC Boost Converter for solar applications
Authors: Mohcine Byar, Et al.
Published: January 2024

An artificial neural network (ANN) adaptive control method for high-gain transformerless DC-DC boost converter is designed in order to achieve better performance and robustness. An ANN controller have the ability to predict the duty cycle in each moment because it is already trained  with a vast number of random values of input, output and reference voltages. In order to minimize the error of obtaining the most convenient duty cycle, a back-propagation algorithm has been used to train the neural network because it has the ability to deal with the weights constantly for the sake of diminishing the loss function which makes the ANN generates the fittest duty cycle. The control scheme has been done for a photovoltaic system and the simulation is done using Matlab/Simulink. Simulation results are given for irradiance, reference voltage and load variations. The results are designated to prove the effectiveness of the proposed control system.
Digital Marketing for Efficiency in Service SMEs: Hybrid Model based on the 4F’s
Authors: Michel Esparza Márquez, Et al.
Published: January 2024

Due to changing global market trends and technological advancements, contemporary businesses experience rapid economic and managerial growth. Those that use digital social networks in their marketing show a positive relationship between market orientation and business results. Digital marketing is crucial for companies by driving innovation and competitiveness, taking advantage of opportunities in digital media and creating strategies to increase sales. However, there is a lack of a model that effectively manages this marketing. The objective of this study is to establish a hybrid model to optimize digital marketing management, through the 4Fs marketing mix (Flow, Functionality, Feedback and Loyalty). Using a mixed method, an instrument was designed that allows evaluating the performance of the 4Fs and establishing a correlation with digital marketing management to establish strategies that contribute to improving the areas of opportunity detected.
Revolutionizing 5G Networks: A Synergy of Routing, Clustering, and Energy Optimization for Unprecedented Performance and Extended Lifespan
Authors: Fathima Rubeena Et al.
Published: January 2024

The concept of revolutionizing 5G (Fifth Generation) networks through a synergy of routing, clustering, and energy optimization is indeed a promising approach to enhancing the performance and lifespan of wireless networks. Exciting changes will occur in the physical, digital, and biological worlds over the next ten years. Although the needs for Beyond 5G (B5G) are not yet fully understood, an effort has been made to stratify 5G progression and B5G. This work highlights the focus on revolutionizing 5G networks through the integration of routing, clustering, and energy optimization techniques. By combining these methodologies, this research work aims to address the complex challenges in 5G networking, such as efficient data routing, resource allocation, and energy consumption. The objective is to achieve both exceptional performance and an extended lifespan for these networks. The proposed work holds promise for significantly enhancing the capabilities of 5G networks, resulting in improved user experiences, optimized resource utilization, and prolonged network lifespan. In order to completely meet the most stringent 5G standards, such as stratification, or deconstruction into existing technologies, will comprise technology scenarios of 5G evolutions. Wireless sensor networks (WSNs), which offer essential data collecting and monitoring capabilities, are made up entirely of 5G networks. These methods are designed specifically for use in 5G networks to increase the network’s lifespan and overall performance. For 5G networks, routing and clustering techniques from WSNs can be modified and optimized to increase energy efficiency and prolong the network lifetime in 5G networks.
Blockage in Coronary Artery Detection and Quantification in Coronary Angiography
Authors: G.R.Jainish Et al.
Published: January 2024

The segmentation of the coronary angiography is extremely crucial in computer-aided diagnosis of arterial motion evaluation. It is difficult to create an automated vessel segmentation method using vascular structures because of the wide range of intensities and noise. The suggested method is an unsupervised method that uses coronary angiogram of the heart as a source and in order to get vascular centerlines, segment vessels, and identify heart vein blockages. First, a preprocessing procedure is utilised to enhance and get rid of the image's low frequency noise using morphological filters and a contrast constrained adaptive histogram equalisation. The extraction of the vascular structure is done using a morphological hessian-based method. The wide and narrow vessels are removed using two distinct scales. After that, the vessel's axis of rotation is extracted. In order to find the bifurcation, it employs a branch detection algorithm. Obstructions are located by considering the diameter across the vessel's cross section.  The efficiency of the suggested method has been evaluated, as evidenced by testing results on a variety of images, achieving an accuracy of 97.08%.
A Computational Model to Predict the Memorability of Web-pages
Authors: Swarup Sethia Et al.
Published: January 2024

In today's digital world, websites are the main point of interaction for a wide range of online activities. As a result, website memorability has become an important topic of discussion. In order to stand out in a highly competitive environment where users are constantly bombarded with information, a website's ability to be memorable is crucial to its success. This study focuses on the development of an automatic computational model for predicting the memorability of a web page. To achieve this, the objects within a web page were identified and their memorability scores were calculated using the ResNet-18 convolutional neural network. The final memorability score of the web page was computed by taking a weighted sum of the areas occupied by these objects on the web page, along with their memorability scores. For the empirical study, 30 web pages from different applications were used to train and test our proposed model. Our model can predict web page memorability with a mean absolute error of 0.077 on a normalized scale of 1.
Underground Attitude MEMS-Based Sensor Technology for Precise Estimation in Mines
Authors: Shailendra Kumar Rawat Et al.
Published: January 2024

In coal mines, precise attitude measurement systems are crucial for enhancing reliability and accuracy. These systems typically integrate multiple sensors to overcome various limitations, such as distance constraints, distractions, and not clear vision. Gyroscopes are employed to tackle these challenges; however, they are very highly critical to slight erosion, which can affect their performance in maximum time. On the other hand, accelerometers do not experience drift but have slower dynamics, posing a between accuracy and response. This study investigates the implementation of a Complemtry filter to offer real-time monitoring of the position and orientation of a mining vehicle in underground mines. The Compatible filter is specifically developed to address various practical challenges, such as sensor output fluctuations and extended periods without receiving local data updates The presentation highlights the application of the Alarm Filter and Kalman filter for bias ratio and mood measurements. The initial focus is on utilizing these filters to measure the yaw angle. By incorporating the Compatible Filter, which employs a data integration algorithm utilizing gyro and compass measurements, a significant improvement in the filter's performance is achieved. This integrated approach simplifies the dynamic modeling of signal sensor activity within the atmospheric network, particularly when dealing with gyro modeling. Applying MATLAB Simulink application k. Additionally, the integration of a magnetic compass and gyro compass into the filter further enhances the accuracy of the sensor device. The primary benefit of this approach lies in the sensor signal independence, making it compatible with any system that utilizes the same set of sensors. By implementing these advancements in attitude measurement systems, underground mines can significantly improve the safety and efficiency of their operations.
BiETech : Bicluster Ensemble Techniques
Authors: Geeta Aggarwal Et al.
Published: January 2024

Various biclustering algorithms have emerged now a days that try to deliver good biclusters from gene expression data which satisfy a particular objective function. Users are lost in finding the best out of these algorithms. Ensemble techniques come to rescue     of these users by aggregating all the solutions and providing a single solution which is more robust and stable than its constituent solutions.  In this paper, we present two different ensemble techniques for biclustering solutions. We have used classifiers in one approach and the other approach uses the concept of metaclustering for forming the consensus. Experiments in this research are performed   on synthetic and real gene expression datasets as biologists are interested in finding meaningful patterns in expression of genes.  The experiments show that both the approaches proposed in the paper show improvement over the input solutions as well as the existing bicluster ensemble techniques.
Exploring Sentiment Analysis in Social Media: A Natural Language Processing Case Study
Authors: Manish Rana Et al.
Published: January 2024

Social media plays an integral role in our daily lives, influencing and reflecting global perspectives through the consumption and creation of content. Platforms like YouTube are incredibly active, with a constant influx of video uploads, views, and comments. While the YouTube app allows us to browse videos and comments, it offers only a limited glimpse into the interests and trends of others. Analysing this vast data pool, encompassing diverse language styles, presents a significant challenge. This article delves into the YouTube Data API and its application in Python for accessing raw data. The process involves data cleaning using advanced Natural Language Processing (NLP) techniques, harnessing Python-based machine learning to explore social media interactions, and automating the extraction of trends and influential factors. The journey towards trend analysis is meticulously detailed, featuring examples that leverage a variety of open-source Python tools.
TBC-K-Means based Co-Located Object Recognition with Co-Located Object Status Identification Framework Using MAX-GRU
Authors: Jayaram C V Et al.
Published: January 2024

In the application of detached object recognition in public places like railway terminals, the recognition of the co-located objects in the video is a more vital process. Nevertheless, owing to the occurrence of multiple co-located object instances, the analysis of the status of the co-located object in the video is a challenging process. Hence, for solving this issue, this paper proposes the Min-Max Distance based K-Means (MMD-K-Means)-centric co-located object recognition with object status identification. Primarily, the input video from the railway is converted to frames. Subsequently, it was improved using Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, Tukey’s Bi-weight Correlation-based Byte Tacking (TBC-BT) and MMD-K-Means clustering are done for the detection and tracking of moving and non-moving objects. Subsequently, the Cyclic Neighbor-based Connected Component Analysis (CN-CCA) process was done from the static and moving object-detected frames for the main and co-located object labeling. Next, it executed the patch extraction for the separate analysis of each instance. At last, the Maxout-based Gated Recurrent Unit (Max-GRU) determined the object status in CN-CCA processed frame with the estimated distance between objects and extracted features from the static objects. The proposed system was then experimentally examined and validated in contrast to the standard methods. The proposed MMD-K-Means achieved a co-located object identification rate of 97.92% in 1184 milliseconds. Next, the Max-GRU achieved 98.13% identification accuracy, and it also achieved excellent results for other performance parameters. The proposed system’s performance is experimentally proved with several performance metrics.
Impact of Parameter Adjustments in IEEE 802.15.6 MAC Protocol for Wireless Body Area Networks
Authors: Wan Haszerila Wan Hassan Et al.
Published: January 2024

The WBAN requires a MAC protocol that complies with the specific requirements and specifications of the body area network communication. Typically, this technology is implemented using the IEEE 802.15.6 standard, which employs the CSMA/CA mechanism. The WBAN confronts the challenge of managing the probability of heavy traffic due to the substantial medical data generated by numerous biomedical sensors. This scenario could lead to network congestion, increasing delay and packet losses, thereby posing a risk of network instability. Hence, selecting appropriate parameters is crucial for developing a MAC protocol to reduce delay and packet losses while enhancing the network throughput. This paper evaluates the performance of the IEEE 802.15.6 MAC protocol by modifying parameters such as packet rate, simulation duration, and the number of sender nodes. The IEEE 802.15.6 MAC protocol implementations are assessed regarding delay, throughput, and packet loss using the Castalia-3.3 framework based on the OMNeT++ 4.6 platform. The outcome of this study indicates that the MAC protocol for WBAN applications can be optimized by configuring the parameters correctly.
A Comprehensive Review Study of Cyber-Attacks and Cyber Security
Authors: Abhilash Maroju, Rohith Vallabhaneni, Sravanthi Dontu Srinivas A Vaddadi,
Published: January 2024

In today's world, the majority of governmental, cultural, social, economic, and commercial interactions and activities take place online. This includes interactions between nations, individuals, NGOs, and government agencies. The threat of cyberattacks and wireless communication technologies has recently become an issue for numerous private organisations and government agencies across the globe. Nowadays, our world relies heavily on electronic technology, and safeguarding this data from cyber-attacks is no easy feat. Cybercriminals target businesses with the intention of stealing money. Cyberattacks may also serve political or military objectives in certain instances. Computer viruses, information breaches, data distribution services (DDS), and other attack vectors are among the causes of these damages. In order to accomplish this goal, different organisations employ different strategies to safeguard against cyberattacks. The most recent information technology data is tracked in real-time by cyber security. So far, academics from all around the globe have suggested a number of ways to either stop cyberattacks in their tracks or at least mitigate the harm they do. A few of the approaches have moved on to the study phase, while others are still in the operational stage. The purpose of this research is to examine the offered approaches, identify their strengths and shortcomings, and conduct a thorough evaluation of the standard advancements made in the area of cyber security.