bluuebunny/crossref_metadata_2025 huggingface dataset:
Managing and Assessing the Quality of E-Learning Teaching by Faculty at Indian Universities
Authors: Chungyun Kim
Published: February 2025

The expansion of the usage of mobile, cloud computing and internet `in the current environment affected all the facets and existence of humans. As a result, electronic learning is spreading its wings in every industry and opening up a lot of options. The scope of education developed and grown dramatically in the past years thus emergence of key factors in development of education in universities. The main objective of the study is to assess Indian universities faculty quality teaching of e-learning in the universities. The validity of different criteria was investigated. The main purpose of the study is creating a valid as well as reliable quality measurement scale of electronic learning in viewpoints of both learners and instructors in an Indian environment. The results of exploratory and confirmatory factor analyses are shown in the covariance model and the structural model, respectively. The covariance model demonstrates that from a faculty’s perspective, collaboration, industry acceptance, and value addition are key elements, whereas from a faculty member's perspective, transparency in assessment, technical, and student participation are important aspects. The scope of study is for assessment of quality of E-learning teaching of faculty at Indian universities through various parameters.
Next-Gen AI Quality Checks: Redefining Data Integrity in Automated Workflows
Authors: Chandrasekhar Rao Katru
Published: February 2025

There is a growing need for strong methods to guarantee the accuracy and reliability of data due to the widespread use of next-generation AI in automated processes. This research delves into new approaches to rethink AI system quality checks, with a focus on context-aware, adaptable, and dynamic validation. Modern artificial intelligence ecosystems are notoriously difficult for traditional data integrity frameworks to manage due to the sheer volume and variety of data streams and continuous learning paradigms used therein. A proactive and scalable quality assurance methodology is proposed by this study by combining state-of-the-art methods including feedback loops, explainable AI, and anomaly detection. Research shows that using these methods greatly improves AI-driven processes in terms of accuracy and dependability while decreasing the likelihood of bias, mistakes, and inefficiencies. Findings from this research highlight the need of continuously improving quality assurance procedures for sustaining credibility and efficiency in the age of intelligent automation. This paper delves into the changing landscape of quality assurance in AI-driven processes, with a focus on how automated workflows must prioritise data integrity. With their reliance on varied, high-volume information and complicated algorithms, next-generation AI systems are dynamic and complex, making traditional quality checks inadequate. In order to guarantee strong data integrity, this study suggests a new AI quality assurance system that combines adaptive mistake detection, predictive analytics, and sophisticated validation techniques. The framework reimagines quality standards in AI operations by using state-of-the-art technologies such as blockchain for traceability and federated learning for decentralised validation. There are noticeable gains in efficiency, accuracy of decisions, and reduction of errors in empirical assessments. The results highlight the need to reconsider quality standards in order to build trustworthy and reliable AI ecosystems, which will allow for their ethical and scalable implementation. Organisations striving to align AI systems with strict quality and integrity requirements in increasingly automated settings might look to our work as a benchmark.
Plant Growth Analysis using IoT and Reinforcement Learning Techniques for Uncontrolled Environment
Authors: Suwarna J. Mhasekar
Published: February 2025

Monitoring and optimizing plant growth in uncontrolled environments, such as open fields or greenhouses exposed to dynamic climatic conditions, pose significant challenges. This study explores the integration of Internet of Things (IoT) sensors with Reinforcement Learning (RL) techniques to enhance plant growth management in such settings. IoT devices provide real-time data on critical environmental parameters, including temperature, humidity, soil moisture, light intensity etc. This data is transmitted to a central processing system, where RL algorithms analyze it to devise adaptive strategies. RL techniques, particularly model-free approaches like Q-learning or Deep Reinforcement Learning (DRL), enable continuous learning and optimization by interacting with the environment. The agent learns to adjust irrigation schedules, nutrient delivery, and artificial lighting based on environmental conditions to maximize plant health and growth metrics. The study presents a framework for deploying this IoT-RL ecosystem, emphasizing cost-effective sensor deployment, data transmission reliability, and energy efficiency. Experiments conducted in simulated and semi-uncontrolled environments demonstrate the system's ability to dynamically adjust to environmental fluctuations, reduce resource wastage, and improve yield outcomes. Key findings reveal that RL algorithms outperform rule-based systems by adapting to non-linear relationships and complex dependencies between environmental variables and plant growth. Furthermore, the framework provides a scalable solution suitable for diverse agricultural contexts, from small-scale farms to large agricultural enterprises. This research underscores the potential of IoT and RL in transforming agricultural practices by offering intelligent, data-driven solutions to manage plant growth in unpredictable environments. Future work will focus on integrating advanced sensing technologies, improving algorithm efficiency, and exploring the system's applicability across various crop types and geographical locations.
70 Identification of Ischemic and Hemorrhagic Strokes with the Aid of Machine Learning and Deep Learning
Authors: Minal Prashant Nerkar
Published: February 2025

Stroke is a leading cause of mortality and disability worldwide, necessitating rapid and accurate diagnosis for effective intervention. This study focuses on leveraging machine learning (ML) and deep learning (DL) techniques to distinguish between ischemic and hemorrhagic strokes using clinical and imaging data. The proposed methodologies aim to enhance diagnostic precision, reduce time to treatment, and optimize healthcare outcomes. A dataset comprising neuroimaging scans (e.g., CT and MRI) and clinical parameters (e.g., age, comorbidities, symptom onset) was analyzed. Various ML algorithms, including random forests and support vector machines, were utilized for feature selection and preliminary classification. Additionally, convolutional neural networks (CNNs) were employed to process imaging data for stroke type identification. Hybrid models integrating clinical and imaging features were developed to achieve a holistic diagnostic framework. Performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were used to evaluate the models. Preliminary results indicate that the deep learning models achieved higher diagnostic accuracy up to 95% compared to traditional machine learning approaches. The hybrid models demonstrated superior performance, showcasing the importance of combining imaging and clinical data for robust stroke diagnosis. This research highlights the potential of ML and DL in revolutionizing stroke diagnosis, addressing the challenges of misclassification and delayed identification. The findings advocate for integrating AI-driven diagnostic tools into clinical workflows to support medical decision-making and improve patient outcomes. Future work involves validating the models on larger, diverse datasets and exploring their adaptability in real-world clinical settings.
Deep learning Models for Trust management in Social Internet of Things
Authors: Anciline Jenifer J
Published: February 2025

Introduction: The growing prevalence of IoT has resulted in both human-to-thing(H2T) communication and thing-to-thing(T2T) communication. In recent years, a new paradigm merging IoT with social networks has arisen, termed the Social Internet of Things (SIoT), where devices are not only intelligent but also socially conscious. Trust is an important element in SIoT and it allows reliable automatic communication between items and trust management is also an essential aspect for secure communications between IoT devices, In the dynamic SIoT environment, a traditional trust framework's static or heuristic approach is ineffective.
Objectives: To study the Deep Learning (DL) Models for trust management in SIoT and to suggest a hybrid model to predict the trustworthiness of the IoT devices in SIoT Networks.
Methods: A combined strategy is suggested in this study using Graph Neural Networks (GNN) and Recurrent Neural networks (RNN) to predict the trustworthiness of SIoT using vast high-dimensional data and detect specific trends in social communications.
Results: The finding shows that the hybrid GNN and RNN model outperforms with an accuracy of 91.8% over the standalone GNN and RNN methods.
Conclusions: The integrated model that includes both GNN and RNN techniques gives better results in terms of accuracy, precision, recall, and F1-score over the other methods assessed separately.  The ability of deep learning to interpret complex multidimensional data and adapt to the highly dynamic nature of social and physical interaction is significant in maintaining trust within SIoT networks.
Ensemble Learning Framework for Mango Plant Disease Detection and Classification
Authors: Meenakshi Thalor
Published: February 2025

Agriculture sector play a vital role in economy of India where the crop of mangoes is also considered as major fruit crop as it contributes significantly to the country's agricultural economy. Mango cultivation provides livelihood to millions of farmers across the country. One of the main barriers to increased food production is the diseases of the plants. Mango trees are prone to a variety of diseases and addressing them effectively can be quite challenging. This paper presents an ensemble-based classification of mango tree leaf diseases. Ensemble based classification makes use of multiple classifiers in order to make an efficient decision about the crop disease. In this paper, homogeneous VGG-19 CNN architecture is employed in bagging manner which proves the validity of the system by providing the accuracy of 95%, precision of 97%, recall of 97% and F-score of 97%.This system will be useful for Ministry of agricultural and farmer welfare for taking preventive measures to make Mango trees disease free.
Starfish Optimization Algorithm for Economic Emission Dispatch with Chance Constraints and Wind Power Integration
Authors: Dileep Kumar Mohanachandran
Published: February 2025

This paper demonstrates the application of the Starfish Optimization Algorithm (SFOA) to solve the Economic Emission Dispatch (EED) problem considering chance constraints, integrating wind energy sources. Wind power is modeled as a Weibull distribution, and the problem is set up in the framework of Chance-Constrained Programming (CCP) to capture the variability of wind energy. The applied chance constraints ensure that the power flow equation is satisfied with a specified probability, and the resulting CCP formulation is transformed into a deterministic optimization problem using the premises of probability theory. Inspired by the peculiar behaviours of starfish, such as exploration, preying, and regeneration, the SFOA inspires strong global search capability and convergence efficiency. This approach effectively minimizes both generation costs and emissions while addressing the uncertainty associated with wind power. The method is tested on a ten-unit power system with wind energy integration, showing the robustness and efficiency of SFOA in achieving optimal solutions for the chance-constrained EED problem.
Design and Implementation of Road Rutting Detection using MAnet with Efficientb0 Architecture
Authors: Radhika Reddy Kondam
Published: February 2025

Road rutting will become a serious problem in transportation infrastructure causing surface deterioration, safety concern, and increased maintenance expenses. The aim of this study is to establish an automatic and efficient detection model for discriminating road rutting, which can overcome the inconvenience of human-made reading with less errors. The present study develops the state-of-the-art knowledge in real-time and computationally efficient models for road rutting detection, focusing on effective operation of these universal tools under complex environments with different lighting conditions and surface material types. In the proposed approach to detect rutting with high accuracy, MAnet and efficientb0 architectures are used in combination. MAnet is an attention mechanism-aware network developed to extract more useful fine-grained features, by capturing the spatial and channel-wise dependencies between input images. Efficientb0: Efficientb0 which is the least size model and very computational efficient that allows our model to do inferences on real-time keeping accuracy unaltered. The experimental results confirm that the proposed model outperforms current state-of-the-art models (DeeplabV3 and U-Net) in performing semantic segmentation tasks for aerial images, obtaining a test set mIoU of 0.865. The experiment results indicate that the MAnet-Efficientb0 model is suitable for application in road maintenance system with high accuracy and computationally efficient.
Semantic Object Segmentation using Modified HRNet Deep Learning Model
Authors: Abdul Saleem L
Published: February 2025

This paper presents a modified High-Resolution Network (HRNet) architecture designed for enhanced semantic object segmentation, addressing the prevalent challenges of accuracy and computational efficiency in complex image analyses. Through strategic modifications to the conventional HRNet, including optimized feature blocks and advanced feature extraction techniques, our model demonstrates significant improvements in segmentation performance. Utilizing the Cityscapes dataset, renowned for its comprehensive urban scene representation, our enhanced HRNet model achieved a notable increase in validation accuracy to 85.8% and mean Intersection over Union (mIoU) to 63.43%, surpassing the original HRNet benchmarks by 3.39% and 3.43%, respectively. These results highlight not only the precision of our model in delineating intricate urban landscapes but also its robustness in handling diverse object scales and complexities. The qualitative analysis, underscored by comparison images, reveals our model's ability to produce more defined segmentation contours and accurate object identifications, setting a new standard for HRNet-based semantic segmentation. This work not only advances the field of high-resolution image segmentation but also offers a foundational model for future research aimed at solving increasingly complex image processing challenges
The Convergence of Computer Engineering and Artificial Intelligence: Exploring Modern Software Developments
Authors: Shaimaa Saadoon Mahmood ALrfae
Published: February 2025

AI and Computer Engineering are combining to revolutionize software development. They encourage creativity, enhance functionality, and increase efficiency. As AI-driven tools and frameworks become indispensable to the development of today, their merging with CE is changing the game and making it possible to create intelligent, adaptable systems that can solve the most challenging problems in multiple industries.
This study aims to investigate where artificial intelligence and computer engineering intertwine in modern software development, specifically by looking into methods that enhance innovation, functionality, and efficiency in software through integrated AI-driven tools, frameworks, and performance measures.A three-phase mixed-methods approach was adopted. The secondary data analysis included pertinent literature and open-source code repositories, structured surveys, and interviews of fifty industry professionals. While qualitative analysis was done by applying thematic coding to find trends, obstacles, and possibilities in AI integration, quantitative analysis focused on performance indicators such as execution speed, accuracy, and memory utilization.
The most widely used AI frameworks are TensorFlow and PyTorch due to their performance and flexibility. The industry-specific trends have been identified with different priorities: healthcare focuses on accuracy (77.5%), retail on the execution speed (55 ms) and minimal resource usage, and finance on balanced optimization. Major challenges were seen in the form of barriers such as a high learning curve (20%), scalability issues (25%), and compatibility issues (30%).The report emphasizes that better tools, increased cross-platform support, and thorough training are needed to overcome the hurdles related to the adoption of AI. It identifies industry-specific needs for AI software optimization and provides useful advice on how to successfully use AI technology in sectors like healthcare, retail, and finance.
Specific Contribution: This study provides practical insights into how AI is revolutionizing software development. It provides a strong analytical framework to assess performance metrics and solve integration issues by analyzing industry-specific trends and trade-offs in AI integration. This advances our understanding of CE-AI convergence and guides future research and development in AI-augmented systems.
Optimization of VM migration and Energy Consumption using Genetic Algorithm
Authors: Harmeet Kaur
Published: February 2025

Introduction: The substantial energy usage in the cloud computing system is a significant drawback both for the cloud providers along with the cloud service users. In order to decrease the amount of energy used, it is necessary to implement virtualization techniques.
Objectives: this paper employs a GA to optimize the migration of VMs and reduce energy usage in a cloud environment.
Methods: The VM consolidation method effectively handles cloud resources, meeting the needs of both cloud consumers and suppliers. Moreover, it helps in enhancing the efficiency of servers while simultaneously decreasing the excessive energy usage in data canters. Nevertheless, the unnecessary activities of the VM consolidation technique result in inadequate VM selection and improper VM assignment, causing low performance, QoS, and violations of SLAs. Data center management struggles with energy consumption. VM migration and placement works well for this. Data centers require energy-saving solutions without affecting other parameters.
Results: The performance of the proposed method has been evaluated utilizing factors like CPU usage, memory usage, network speed, power usage, and SLA breaches.
Conclusions: The comparative analysis of the proposed approach with existing methods highlights its effectiveness and trustworthiness.
Entrepreneurial Marketing: Implementation of Innovation Dimensions in Local Wisdom-Based SMEs to Support Export Success
Authors: Siti Nurul Khaerani
Published: February 2025

This article discusses the implementation of product, technology, and marketing innovations, which are part of the dimensions of entrepreneurial marketing in local wisdom-based Small and Medium Enterprises (SMEs) to support export success. The method used is a qualitative phenomenological approach. Data collection techniques employed purposive sampling, examining six local wisdom-based SMEs oriented towards export in West Nusa Tenggara, Indonesia. The informants in this study include SME owners/managers, consumers, employees, and artisans. Data analysis was conducted using NVIVO-14 software. This article proves that SMEs innovate their products by searching for ideas and sources of inspiration, resulting in innovations that enable the products to survive and compete in the export market. Technological innovation is limited to online sales, as the products of these SMEs are characterized by high-quality handmade features. SMEs innovate their marketing by using digital marketing and emphasize the use of social media as a communication tool to engage with consumers. The implications of this study suggest that SMEs that adopt innovative approaches in various aspects of their business are able to enhance their competitiveness and achieve greater success in the export market.
Enhancing Understanding of Renewable Energy Power Plant Concepts Using Virtual Reality (VR) in Higher Education
Authors: Rahmaniar
Published: February 2025

The complexity of renewable energy concepts is a significant challenge for electrical engineering students, especially in the visualization of power generation systems. This research investigates the implementation of mobile virtual reality (VR) technology to improve students' understanding of new and renewable energy concepts at a university in North Sumatra, Indonesia. The study involved 30 first-year engineering students who participated in a structured mobile VR learning program using smartphone-based VR headsets, which combined quantitative and qualitative approaches through pre- and post-test assessments, satisfaction surveys, and group discussions. The results showed a significant increase in student understanding (t(29) = 24.86, p < 0.0001), with factor analysis revealing three main components: experiential impression (eigenvalue = 3.2), content interactivity (eigenvalue = 2.7), and relevance to concepts (eigenvalue = 2.1). A positive correlation was found between students' comfort level with mobile VR technology and their score improvement (r = 0.54, p = 0.0021), while 87% of participants reported that the integrated group discussion increased their engagement. This study provides practical insights for educators looking to improve visualization of technical concepts through immersive learning experiences.
Development of an Evaluation Model for Industrial Internship Programs
Authors: Ambiyar
Published: February 2025

Evaluation of industrial internship programs is crucial to assess their effectiveness and ensure alignment with the needs of the workforce. This study arises from several pressing issues, including the lack of competency alignment between vocational education and industry, significant gaps in the design and implementation of apprenticeship programs, and the reliance on conventional evaluation methods that lack technological integration. The research aims to address these challenges by developing a robust evaluation model tailored for industrial apprenticeship programs in Indonesia and Malaysia. Drawing upon established frameworks such as the CIPP model, Countenance Model, and Kirkpatrick evaluation model, this study incorporates innovative elements, including an adaptive, technology-driven evaluation system designed to enhance assessment accuracy and relevance. The novelty of this research lies in its focus on creating a link-and-match ecosystem between vocational education and industry, leveraging advanced evaluation tools to provide actionable insights for improving program quality and policy decisions. Using interviews, questionnaires, and field observations, data were collected from a diverse group of vocational university students, lecturers, and industry representatives. The findings not only validate the urgent need for an enhanced evaluation model but also demonstrate its potential to elevate the preparedness of graduates, equipping them with the skills and competencies required to succeed in the job market or as entrepreneurs. The final deliverables include a comprehensive evaluation framework, a technology-based assessment application, and a handbook to support stakeholders in adopting and implementing the model.
Role Model of Mining MBKM in Vocational Education
Authors: Bambang Heriyadi
Published: February 2025

Vocational education in Indonesia has not been optimally implemented in the world of Indonesian education. Unemployment from graduates of vocational schools, polytechnics or vocational education because the quality does not always meet the qualifications. Vocational education or vocational education has problems in the implementation and implementation process, one of which is related to the Merdeka Belajar Kampus Merdeka (MBKM) curriculum. The problems that occur are that the MBKM curriculum has not been implemented optimally, the lack of socialization of the MBKM curriculum, the MBKM curriculum is not yet relevant between universities and industries that are linked and matched, there are still obstacles to the implementation of the MBKM curriculum such as not being effective, inadequate human resources, minimal information on MBKM technical guidelines, and the absence of a role model for implementing the MBKM curriculum that is appropriate and considered effective. The objectives to be achieved from this study are a role model for implementing the MBKM curriculum in mining vocational education. Analyze actual data and produce concepts, preparation, planning, implementation to assessment of the MBKM curriculum. The successful implementation of the MBKM program requires clear preparation, planning and stages. However, in actual conditions, vocational education still has difficulty implementing the MBKM curriculum ideally. The importance of clear direction, preparation, planning, implementation to assessment. This study uses a mix method that combines qualitative and quantitative research. The subjects of the trial were Mining Engineering students at Polytechnics and universities throughout Indonesia, and involved industry and experts. For qualitative data analysis techniques obtained from interviews, data descriptions and quantitative analysis were carried out.
Unraveling the Nexus: A Deep Dive into the Relationship between Crypto Currency and Commodity Market Volatility
Authors: V.N. Sailaja
Published: February 2025

Due to anonymity of users, low transaction costs, no middleman, and a rise in accepting Bitcoin payments, cryptocurrencies might seem attractive as a medium of exchange to potential consumers (Katsiampa, 2019). However, the Crypto Currency market is still under rapid development and evolution, the prices of currencies change drastically (Dr N Saraswati, 2022). Commodity market investment grew rapidly in the mid-2000s and attracted investors to hedge the risk against price fluctuations (Scott Main, Scott H Irwin, 2018). The Present research explores the hypothesis that Crypto Currency Returns exhibit volatile behavior, while Commodity Returns do not. Additionally, it investigates the relationship between Trading Volume and Returns in both markets, as well as the potential relationship between major Commodities and Crypto Currency Returns. The study utilizes a comprehensive dataset comprising historical price data from 2017 to February 2023 for Crypto Currencies and Commodities. Statistical methods, including standard deviation, Auto Regressive conditional heteroscedasticity employed to measure Volatility. The analysis will also examine the Correlation between Trading Volume and Returns in each market and explore the relationship between Commodities and Crypto Currencies by using co-integration test and Granger Causality tests. The findings of the study surely contribute to the understanding of the Volatility characteristics of both markets and their implications for investors. The findings of the study indicated that Commodities, such as Gold and crude oil, did not have a significant impact on Crypto Currencies. The analysis of Trading Volume and Returns revealed no substantial relationship between the two asset classes.
A Study on Exploring the Nexus between Economic Value Added (EVA) and Equity Capital
Authors: V.N. Sailaja
Published: February 2025

This paper investigates the intricate relationship between Economic Value Added and equity capital, aimed to determine their synergies in evaluating a company's financial performance. It is a metric designed to measure the company’s economic profit by considering the cost of capital, serves as a pivotal indicator of its wealth creation capabilities. Conversely, equity capital constitutes the primary funding source from shareholders, representing a fundamental aspect of a firm's financial structure and ownership. The primary objective of this study is to analyse the relationship between Economic Value Added and equity capital in select companies listed in BSE Sensex, observing the underlying dynamics that govern a company's efficacy in utilizing shareholder funds to generate sustainable profits. The present study also focused on the impact of equity capital on Economic Value Added and to forecast the Economic Value-Added trajectory for these entities in forthcoming years. The findings of the study reveals that there is relation between equity capital and economic value added for various companies in both positive and negative ways, as well as the significance of autocorrelation in decision-making. Additionally, the impact of equity capital on economic value added was established in Reliance, ICICI, ITC, HCL, Dr Reddy's, and Axis Bank. Lastly, the ARIMA model provided insights into the significant growth in from 2023 to 2031 in ITC, L&T, SBI, Mauritius, Nestle, and Wipro including a notable turnaround for HDFC Bank in the later years.
To Identify Enablers to Achieve Food Security with IoT and Blockchain Technology and Revolutionise the Green Food Supply Chain Systems
Authors: Ramesh Babu M
Published: February 2025

The COVID-19 pandemic made the world encounter significant disruptions that heightened the previously faced issues associated with food security. The aspects of food safety, quality, and accessibility are impacted in such a manner, necessitating green food supply chain management (GFSCM) supported by technologies like blockchain and IoT. Nine GFSCM enablers identified and critically discussed in the context of better food security through the attainment of SDG 2. Based on the ISM technique, this study analyses the relationships and influential factors among these facilitators. The findings have emphasized that blockchain and IoT are an essential mechanism for promoting transparency and traceability and reducing food waste in the supply chain of food. Adding to the present literature, this paper develops a conceptual framework that helps managers implement digitization enablers and thus support sustainable food security. These pave the way toward the success of the United Nations in achieving sustainability through food quality and safety.
Transformers in Sentiment Analysis: A Paradigm Shift in Deep Learning Research
Authors: Kumar Puttaswamy Gowda
Published: February 2025

It was the Transformers which changed the paradigm for the sentiment analysis, sending shock waves to deep learning with its architecture and unprecedented effectiveness. An attention mechanism abstracts the significant features of the input in the self-attention layer, leading to a reconsideration of both pre-trained models, such as BERT, RoBERTa and GPT, at all levels of the sentiment analysis pipeline. These models utilize self-attention mechanisms, allowing them to capture syntactic and semantic dependencies more effectively than recurrent and convolutional networks, leading to significant improvements in various NLP tasks. A rigorous methodology was applied, including fine-tuning pre-trained transformers on heterogeneous datasets and comparing their performance against state-of-the-art methods. The numerous experimentation showed the improvements in terms of accuracy, precision, and recall in some domains such as customer reviews, social media sentiments, and financial data analysis. The study reveals key findings demonstrating the adaptability of the models to solve domain-specific challenges with transfer learning and their efficiency in handling imbalanced datasets. More than that, the paper also describes the trade-offs between computational cost, scalability, and other aspects to consider when implementing transformers in practice. With effective syntactic and semantic embeddings being learned by transformer-based models, this study demonstrates that such deep learning-based architectures redefine performance standards for sentiment analysis tasks and serve as promising basis for building even better interpretable superclass models, suggesting their tremendous potential in shaping current and future research trends in both natural language processing and beyond.
Unfolding the Rise of Direct Selling Formats in India: An Explanatory Analysis
Authors: Aisha Ibrahim Mohammed
Published: February 2025

The Indian direct selling industry has been analysed using a framework developed from the environmental theory of retail evolution, relationship marketing theory, and Porter's five forces model. The developed heuristic framework provides a basis as to how retail institutions adapt to changes subject to the changes occurring in the firms' environment subject to the constraint that firms in the long run earn profits by maintaining long-term relationships with the consumers such that the so-called consumers turns into the patrons of the company. The identified factors in the firms’ environment are consumers, competitors, suppliers, public policy factors and information technology factors.   Through the descriptive analytical analysis, it has been found that direct selling in the Indian context has evolved due to the changes happening in the direct selling’s operational milieu wherein these firms adapt to changes that happening in India’s macro and microenvironment. The opening up of the economy, and changing consumer demographics have been the driving force behind the direct selling industry in India. However, the public policy factors through the enactment of regulations have been a key catalyst that helps the business in protecting consumers' interest, warding off illegal competition, with the support of MSME coupled with advancements in information technology that transformed the business operation whilst ensuring transparency. Though the environment has been conducive to the growth of the direct selling industry, the industry has faced huge setbacks in terms of litigation.
A Novel Framework for Extracting and Recognizing Text in Scene Images through Self-Attention CNN Enhanced with Fuzzy DNN
Authors: Senu Jerome
Published: February 2025

Detection and deriving textual information from images help in better understanding of the information it contains. The extracted text can serve as input to many computers vision-based applications. Text retrieval from images of natural settings is quite challenging because of noise interference, poor lighting conditions and obscuration by other objects in the scene. A method for extracting text from images is suggested, employing a combination of Self-Attention Convolutional Neural Network (SAT-CNN) and Fuzzy Deep Neural Networks (DNN). This approach aims to achieve highly accurate text extraction from images by minimizing error rates. The input images undergo efficient preprocessing with a trilateral filter to eliminate noise and enhance image quality. It is crucial to accurately identify and separate the foreground from the scene image to isolate the text after effective feature extraction. Subsequently, the extracted features are fed into the Self-Attention-based Convolutional Neural Network to discern between text and non-text components. During text classification process, the misclassification rate is minimized with the help of metaheuristic Human Mental Search Algorithm (HMSA). After identifying text and non-text components, the character recognition from text is done using Fuzzy Deep Neural Network with Sparse Auto encoder (FDNN-SAE). The proposed framework attains higher accuracy compared to existing methods, such as WNBC–AGSO-TE, HDNN–AGSO-TE, and GAN-TE respectively.
Literature Review on Road Damage Detection and Severity Recognition: Leveraging Computer Vision
Authors: Jonathan Estilong
Published: February 2025

The increasing demand for quick and precise road maintenance has therefore been underscored by extensive research that was created over the last years, to find automated systems in which computer vision can work — harnessing deep learning methodologies to detect defects on roads & figure out how severe these actually are. It provides a discussion of different methodologies and technologies in these pipelines such as Convolutional Neural Networks (CNNs), YOLO models, ensembling learning etc. They have a good performance in various kinds of road damage: cracks, potholes, and surface deformations identification with high accuracy. They also use methods like image tiling, transfer learning, and multiple spectral data to increase their robustness so that models can be used effectively in different environmental conditions.But the avoidance of problems is still a long way off. Nonetheless, the disparate road scenarios across geographies and absence of labeled high-quality datasets remained as some common bottlenecks in addition to real-time-driven requirements that mandate significant compute powers. Although much more headway has been made in recognizing severity (using segmentation approaches and multimodal data, in particular), the combination of detection and classification altogether within a single joint pipeline is significantly harder.A comprehensive analysis of state‑of-the‑art software tools, recent challenges, and advances to improve the detection/ severity identification capabilities of road damage systems. It emphasizes how to address data problems, enhance model generalization capability in unseen scenes(a.k.a novel), and minimize latency. One final takeaway is the valuable reminder of the need to create operational standards for classifier performance required to drive practice adoption and scale in larger deployment settings ideally incentivizing public-private partnerships.
Graph LRP and Domain Adversarial Neural Networks: An Approach to evaluate the performance and Design an Iterative method for Lumpy skin disease prediction
Authors: Sandeep Sharma
Published: February 2025

The rising incidence of Lumpy Skin Disease in livestock is a threat to animal health and agricultural economies worldwide. Traditional approaches towards the prediction of this disease often fall short of expectations in explain ability, cross-domain adaptability, and real-time responsiveness for effective intervention. The authors mentioned the limitations by introducing a comprehensive machine learning framework LSD prediction. Specifically, graph neural networks combined with layer-wise relevance propagation, Graph LRP, to make the model more interpretable and transparent about the model's decision process. Graph LRP assigns a relevance score for input features. DANN generalizes it better and reduces labeled data requirements. Afterwards, to ensure adaptability in real-time, TinyML-based approach is followed by using the lightweight MobileNetV3 with GCNs for edge device deployment. This will enable low latency and efficient predictions to be made using continuous IoT sensor data streams and satellite images. Our proposed model improves the aspect of interpretability by 85% in relevance precision and cross-domain accuracy up to 95%, real-time inference performance at a 70-80% reduction in inference time. These make for a robust, scalable solution for the early detection and intervention of diseases, hence conferring benefits of magnitude in LSD management and control across different environments.
Predicting Optimal Chemotherapy Regimens in Breast Cancer Treatment using Machine Learning
Authors: Jayshree Bedade
Published: February 2025

Breast cancer treatment involves personalized chemotherapy regimens to improve patient outcomes, and selecting the optimal regimen is a crucial challenge. Machine learning (ML) algorithms offer potential solutions by analyzing complex clinical data and predicting effective treatment strategies. This study conducts a comparative analysis of multiple ML algorithms, including decision trees, support vector machines, neural networks, and ensemble methods, to predict the optimal chemotherapy regimens for breast cancer patients. Using a dataset comprising clinical, pathological, and molecular features, the models are trained and evaluated based on accuracy, sensitivity, specificity, and predictive power. The results demonstrate that ensemble methods outperform other approaches, offering higher prediction accuracy and robustness. Feature importance analysis further highlights significant predictive factors for chemotherapy response. This comparative study provides valuable insights into the strengths and limitations of various ML algorithms in the context of personalized chemotherapy for breast cancer, contributing to more informed decision-making and potentially enhancing treatment efficacy.
Accelerating Intrusion Detection Dataset Analysis- A Framework Using AutoGen Agents for CIC-IDS 2017
Authors: Nitin W. Wanhade
Published: February 2025

An IDS is a vital component in securing any network, however, the practical operation of an IDC is often dependent upon reasonable response times for the data with a huge volume. In this paper, we attempt to enhance the analysis of the CIC-IDS 2017 dataset using AutoGen, a deep learning model framework related to state-of-the-art. AutoGen performs a lot of the work automatically without requiring human intervention bottlenecks such as data preprocessing, feature engineering, or even model training thus saving a lot of time and work when developing an IDS. We compared the performance of AutoGen against prompt-based language models by focusing on task completion metrics along with three additional metrics: Humane Evaluation score, time taken, and resource overhead. The results exhibited that AutoGen is far superior to conventional ones in every way possible. In summary, the findings of this study demonstrate AutoGen’s popularity for the future of intrusion detection through its data analysis function in the bias of the entire system performance parameter.
Marketing Research of the Territory's Image: On the Example of the Issyk-Kul Region of Kyrgyzstan
Authors: Nukeeva Medina Anarbekovna
Published: February 2025

This article discusses the process of determining a favorable image and development of a territory (region), as well as its practical development and further functioning. The image of a region is a spontaneously formed and/or artificially created image of a region that is steadily entrenched in the mass consciousness, under the influence of a set of emotional, rational characteristics, beliefs and feelings of people who arise about the characteristics of a given territory. Image is one of the determining factors of the perception of the territory. The image of the territory allows local authorities effectively to interact with various target groups: the population, tourists, investors, etc. A positive image of the territory is an important element of ensuring socio-economic stability.This article is devoted to the study of the image of the Issyk-Kul region of Kyrgyzstan. Issyk-Kul region is a region located in the central part of Kyrgyzstan. It got its name from Lake Issyk-Kul, which is one of the largest mountain lakes in the world. The region is known for its unique nature, picturesque mountain landscapes, clean air and diverse types of vegetation.
Detecting Brain Tumors Using an Adapted DE Algorithm with the Otsu Technique
Authors: Meera Ramadas
Published: February 2025

Brain tumors require precise and prompt detection techniques because they pose a serious challenge to medical diagnosis and treatment. In order to improve brain tumor diagnosis from medical imaging data, this work presents a novel method that combines the Otsu methodology with a customized Differential Evolution (DE) algorithm. For segmentation, the Otsu approach is used for accurate thresholding, and the DE algorithm is specifically tailored to improve the process. This study assesses the efficacy of the suggested methodology through thorough testing and analysis using a variety of datasets, including MRI and CT scans. Comparing the results to traditional methods, it is clear that the accuracy of tumor detection and the quality of segmentation have significantly improved. The integrated approach exhibits favorable outcomes for advancing computer-aided diagnostic systems, offering medical practitioners a reliable means for early tumor identification and subsequent treatment planning.
Factors Affecting Students’ Academic Performance Through Online Learning During Crisis Situation in Myanmar
Authors: Aye Aye Aung
Published: February 2025

Online learning has become a critical educational platform during the crisis situation in Myanmar. Several students access online learning platforms, and academic institutions implement different online training programs. This study analyzes the factors affecting students’ academic performance through online learning during the crisis in Myanmar. The study used a quantitative research design and collected data from 68 students. The results showed that technology literacy and individual study habits directly affect students’ academic performance. However, lecturer-related and self-regulatory factors have no significant effect. This study recommended learning technology skills and building self-study habits to succeed academically.
The Influence of Network Coverage Image and Digital Marketing Promotion on Churning Intention Mediated by Digital Satisfaction and Moderated by Private Identity: Approach on Telecommunication Customers in Indonesia
Authors: Abdullah Kafabih
Published: February 2025

The telecommunications industry is a key sector contributing significantly to the national economy. Amidst intense competition among telecommunications operators, many telecommunications customers tend to switch to other operators (Churning). The objective of this research is to investigate how Network Coverage Image and Digital Marketing Promotion impact Churning Intention, with Digital Satisfaction acting as a mediator and Private Identity as a moderating factor. The research targeted telecommunication customers from all operators in Jakarta who had experienced switching SIM cards to another operator. A total of 386 samples were gathered, and the data were analyzed using SmartPLS 3.0. The study's findings reveal that Network Coverage Image and Digital Satisfaction significantly reduce Churning Intention, whereas Digital Marketing Promotion significantly increases it. Private Identity, however, does not significantly influence Churning Intention as a moderating variable for Network Coverage Image. This research offers a novel contribution by expanding the understanding of the push-pull-mooring (PPM) theory as it relates to customer churn intentions in the telecommunications industry, specifically by examining the effects of network coverage image, digital marketing promotion, digital satisfaction, and customer private identity.