bluuebunny/crossref_metadata_2025 huggingface dataset:
A influência das tradições culturais no processo de inclusão escolar de jovens transexuais em Mato Grosso do Sul
Authors: Paes Ademilson Batista , Santana Everton dos Santos de , Silva Soraya Maria Batista da
Published: February 2025

A inclusão de jovens transexuais no sistema escolar de Mato Grosso do Sul (MS) é fortemente influenciada por tradições e valores culturais. Essas tradições, que combinam elementos religiosos e econômicos, têm um impacto significativo em como as escolas abordam a diversidade de gênero. A rigidez cultural no MS está presente tanto nas práticas escolares quanto no ambiente familiar, essa inflexibilidade não está presente apenas no ambiente escolar, mas também na estrutura familiar mais ampla. A religião desempenha um papel central na manutenção de normas conservadoras de gênero, com igrejas e professores frequentemente reforçando esses valores em suas práticas de ensino. A cultura do agronegócio também impacta a inclusão dos jovens trans no MS. Esse cenário rural promove uma visão tradicional e resistente dos papéis de gênero, com jovens trans frequentemente vistas como "desviantes" ou "inadequadas". Apesar dessas barreiras culturais, há nuances que podem promover a inclusão dos sujeitos trans, e compreender essas nuances é crucial para desenvolver estratégias de inclusão eficaz.
“Deve valer alguma coisa”: análise de Borges pelo método da engenharia reversa
Authors: Peron Eduardo , Hergesel João Paulo
Published: February 2025

O formato televisivo conhecido como sitcom tem sua estrutura determinada pela poética aristotélica, conforme comprovado por manuais de roteiro norte-americanos e estudos sobre obras estadunidenses. Este artigo tem como objetivo verificar a presença e a influência de fórmulas narrativas clássicas na produção contemporânea brasileira, tendo como objeto de estudo o primeiro episódio de Borges. Para isso, adota-se o método da engenharia reversa, em interface com a análise poética do audiovisual. No corpo teórico, propõe-se um diálogo entre Duarte (2008), Austerlitz (2014) e Pellegrini (2014; 2015), no que se refere à história e ao conceito, e entre Bordwell (2008), Rodrigues (2014) e McKee (2015), no que se refere à produção. Como um dos resultados, viu-se que Borges apresenta características do sitcom moderno, mas inova ao trazer em seu conteúdo fenômenos tipicamente brasileiros, atrelando o produto a seu contexto cultural.
A role of medical­and­social factors is in development of the combined forms of infertility
Authors: Shchedrov Andrey
Published: February 2025

The objective: to learn modern medical-and-social features at infertility in a matrimonial pair.Materials and methods. There was surveyed 100 matrimonial pair with infertility, a representative selection was here formed in quality and amounts with the use of method of systematic random sample.For the study of medical-and-social description of patients, exposure of facts of risk, determination of level of knowledges through question of maintaining and restoring in a reproductive health a statistical method was used, method of copies from ambulatory maps, survey of patients. In addition, on this stage of research by us social, economic and domestic factors which determine a reproductive health were certain. 133126 виченеясуачиа62х6емдкченеясуовід наукових досліджень до практики | Perinatology and reproductology:from research to practiceNo4-1 Vol. 4 • 2024ISSN 2788-6190БЕЗПЛІДДЯResults. The results of the conducted researches testify that medical-and-social factors matter very much for the decision of question of realization of reproductive function in matrimonial pair. A basic value is had the state of reproductive health of not only woman but also man or partner, and also maintaining of stage of grant of the specialized medical care, beginning from women’s consultation and concluding the specialized clinics of assisted reproductive technologies. Conclusions. The got results must be taken into account at the grant of medical care to the matrimonial pair with violation of reproductive function.Keywords: combined forms of infertility, medical-and-social factors, assisted reproductive technologies.
ПСИХОЛІНГВІСТИЧНІ МЕХАНІЗМИ ВПЛИВУ ЕМОЦІЙНОГО ІНТЕЛЕКТУ НА КОМУНІКАТИВНУ ЕФЕКТИВНІСТЬ
Authors: ГУДЗЕНКО О. Г. , ПАСТРИК Т. В. , ЛАЗЬКО А. М. , ГАЛЬЧУН Н. П.
Published: February 2025

У сучасному динамічному світі комунікація стає ключовим інструментом соціальної взаємодії, професійного розвитку та особистісної реалізації. Представлена наукова стаття присвячена комплексному дослідженню психолінгвістичних механізмів впливу емоційного інтелекту (ЕІ) на особливості мовленнєвої поведінки та комунікативної ефективності. Актуальність дослідження зумовлена необхідністю глибшого розуміння взаємозв’язку між емоційним інтелектом, мовленнєвими стратегіями та якістю міжособистісної комунікації. Наукова розвідка ґрунтується на теоретичному аналізі та узагальненні сучасних психолінгвістичних досліджень, що розкривають багатогранний вплив емоційного інтелекту на комунікативні процеси. У статті системно розглянуто ключові аспекти взаємодії емоційного інтелекту та мовленнєвої поведінки: від адаптивності мовлення до формування емпатійних комунікативних стратегій. Дослідження демонструє, що особи з високим рівнем емоційного інтелекту володіють унікальною здатністю до гнучкої мовленнєвої адаптації, точного розпізнавання емоційних станів співрозмовників та ефективної вербальної й невербальної комунікації. Особливу увагу приділено аналізу впливу ЕІ на розвиток емпатії, що виявляється у здатності не лише сприймати, але й глибоко розуміти емоційний контекст спілкування. Результати дослідження підтверджують визначальну роль емоційного інтелекту у формуванні комунікативної компетентності, особливо в освітньому та професійному середовищах. Науковці наголошують на необхідності впровадження спеціальних програм розвитку ЕІ, які сприятимуть підвищенню ефективності міжособистісної взаємодії, зниженню конфліктності та створенню сприятливого комунікативного простору. Подальші перспективи дослідження включають вивчення впливу ЕІ на невербальні аспекти комунікації, культурні особливості комунікативної поведінки та потенціал цифрових технологій у розвитку емоційно інтелектуальної комунікації.
A case of neuropsychiatric lupus in a patient presenting with sepsis
Authors:   Philadelphia College of Osteopathic Medicine, Georgia Campus, Suwanee, Georgia, USA., Soni Karan
Published: February 2025

Systemic Lupus Erythematosus (SLE) refers to an autoimmune condition of multifactorial origin. While SLE most commonly causes con- stitutional symptoms, complications involving the cardiorenal and nervous systems may also occur.
Strongyloides stercoralis: A threadworm parasite causing intestinal infection
Authors:   Parasitology Department, Mohammed V Military Training Hospital, Rabat, Morocco., Hamraoui Amine El
Published: February 2025

We report the case of a 47-year-old male, native and resident of the Republic of the Congo, who was visiting Morocco for a work mission.
Predictive Stress Management for Employees Using Cloud Computing and Machine Learning
Authors: Rajesh Kumar
Published: February 2025

Currently, workplace stress is a prevalent occurrence among employees in organisations. Management must assess the mental state and physical capabilities of personnel prior to task assignment. The constantly evolving demands of the operational environment might elevate stress levels, particularly for individuals who continually operate under pressure. While pressure can enhance performance, excessive pressure may induce stress, adversely affecting the workforce. Workplace stress is an escalating issue within businesses, as it adversely impacts both employees' professional and familial lives. Work stress denotes the pressure or tension individuals experience in their lives. This is seen as a significant factor affecting organisational welfare and personnel health. It influences employee behaviour in organisations. It significantly influences employee motivation and satisfaction. Employee productivity and the overall productivity of the company are influenced by stress and motivation levels. Occupational stress induces a range of psychological issues, including anger, despair, anxiety, impatience, and tension. These factors significantly influence employee motivation.
Multi-Modal Fusion Techniques for Improved Diagnosis in Medical Imaging
Authors: Adil Ibrahim Khalil
Published: February 2025

Identifying diverse disease states is crucial for prompt and efficient clinical management. Complementary data from many medical imaging modalities, including MRI, CT, and PET, can be integrated to improve diagnostic performance. This work aims to assess how well multi-modal fusion methods work to enhance medical picture diagnosis. A multicenter study was conducted with 150 patients with different clinical conditions (mean age 58.2 ± 12.4 years, 52% female). After gathering data from MRI, CT, and PET scans, structural, functional, and textural characteristics were removed from each modality. The three fusion strategies studied were fusion through concatenation, fusion through kernels, and fusion through attention. The fused features were used to train classification models such as Convolutional Neural Networks (CNNs), ensemble techniques, and Support Vector Machines (SVMs). ROC analysis was utilized to assess the diagnostic performance. The multi-modal fusion techniques outperformed the single-modality methods in diagnosing performance. Attention-based fusion yielded the top AUCs of 0.92, 0.89, and 0.91 for brain tumors, neurodegenerative diseases, and cardiovascular conditions, respectively. This significantly improved (p<0.05) compared to the AUC of the best single-modality models. Multi-modal fusion methods are powerful for combining data from various imaging modalities to improve diagnostic accuracy for various medical conditions. These findings highlight the advantages of combining information sources to improve clinical judgment and patient care.
Load Balancing Based Intelligent Software Defined Networking (SDN) Controller
Authors: Liqaa Saadi Mezher
Published: February 2025

The data plane and the control plane are divided by the Software Defined Networking (SDN) paradigm, where an SDN controller governs the operation of the switches it controls while also receiving demand from associated switches. Switches are dynamically controlled by their respective reassigned to be able to distribute the volume of work among controls for SDN. The majority of creative solutions for assignments require a crucial component of collect data regarding switches that need to be reassigned in order to execute load balancing. An individual super controller is utilized data from all switches and gathers information for all controllers, there is a scalability issue that arises as the number of controller’s increases. When assigning switches to controllers in a big network, distances between them can occasionally be a problem. To solve the above problem, this study proposed an intelligent clustering network based on k-means clustering. Under the OpenFlow protocol, Forwarding Table Management Standard Flow Table, and Flow Management, the advantages demonstrate the intelligent network of control concentration. Therefore, the goals were to balance the load by adjusting the cluster node load method and comparing the performance with the traditional controller cluster and single controller. The experimental results reveal that the k-means clustering method proposed in this study can effectively reduce the packet loss rate or delay. And theoretical research shows that our approach delivers a close to ideal result. According to simulation results, our dynamic grouping enhances stable clustering by scaling factor of five, which is significant.
Diversifying Asset Management in Legacy Planning: A Comparative Analysis of Alternative and Traditional Assets for Long-Term Financial Well-Being
Authors: Vijai. Pillarsetti
Published: February 2025

Effective legacy planning necessitates a comprehensive approach to asset management, ensuring long-term financial well-being for future generations. This study investigates the role of diversification in legacy planning by conducting a comparative analysis of alternative and traditional assets.This study also explores the significance of diversification in asset management within the context of legacy planning, focusing on a comparative analysis of alternative and traditional assets to enhance long-term financial well-being. As individuals seek to secure their financial legacy, understanding the roles that different asset classes play becomes crucial. Traditional assets, such as stocks, bonds, and mutual funds, have long been the cornerstone of investment strategies. However, alternative assets—such as real estate, private equity, and commodities—are gaining traction due to their potential for higher returns and lower correlation with traditional markets.This research employs a quantitative approach to evaluate the performance, risk-adjusted returns, and diversification benefits of both asset categories. By analyzing historical data and current trends, the study aims to provide insights into how a balanced approach that includes both alternative and traditional assets can optimize financial outcomes. The findings underscore the importance of incorporating alternative assets into legacy planning strategies, highlighting their potential to mitigate risks and enhance overall portfolio performance. Ultimately, this study serves as a guide for individuals and advisors seeking to construct resilient investment strategies that align with long-term financial goals and legacy aspirations.
Leveraging RegNetX002 for Automated Classification of COVID-19 and Other Lung Opacities in Chest X-ray Images
Authors: Richa Sharma
Published: February 2025

The global corona virus epidemic has affected medical treatment. Because there are so many variations of newly developing infectious viruses, both the dangers they pose, and the effectiveness of vaccinations need much attention. To better comprehend this illness and investigate its transmission, individual diagnosis, and maybe other fascinating related concerns, it is helpful to learn more complex and interpretable models using COVID-19 data. On the other hand, the lack of accurately classified data is creating certain complications and challenges. Utilizing pre-trained Deep Neural Network (DNN) models on big datasets such as ImageNet has been done in earlier publications. This study evaluated the X002 version of the RegNet CNN architecture. Due to its less computationally costly design and very small number of parameters, the RegNet model is more computationally feasible for imaging applications compared to other CNN models. Self-regulation is a regulatory module that RegNet employs; it retrieves spatio-temporal data from the network's intermediate levels. Furthermore, RegNet's weight residual connections, batch normalization, and regularization mechanism approaches make it fast, scalable, and versatile. Our suggested approach successfully classified COVID-19, normal, pneumonia, and other lung opacities with an astounding accuracy of 95.37%, as determined after thorough testing and review.
Enhancing Technical Training Program for ASNAF Trainees through Educational Technology: A Case from the Technical and Vocational Training Centers
Authors: M.N.M Nawi
Published: February 2025

This study investigated the role of educational technology in enhancing technical training programs for Asnaf trainees at the technical and vocational training centers. Addressing the critical need for effective vocational training aligned with industry requirements, this research employed a qualitative case study approach involving in-depth interviews and direct observations at the centers. The study examined how digital tools and technology influence trainees’ learning experiences and their practical skill application, as well as evaluating the training program’s effectiveness in meeting industry needs and improving employability. Findings revealed that educational technology, combined with hands-on projects and interactive tools, significantly enhances trainees’ engagement and practical skills leading to better learning outcomes. However, challenges such as insufficient administrative support, financial constraints, and the need for updated curriculum remain. This study highlighted the importance of aligning training programs with industry demands, and investing in infrastructure and professional development to bridge the skills gap. Recommendations include enhancing industry collaboration, increasing financial support for entrepreneurial ventures, and standardizing training curricula across centers. These measures are crucial for improving training effectiveness and ensuring that asnaf trainees acquire relevant skills that boost their employability and career prospects.
SARSA-Based Reinforcement Learning Framework for Energy-Aware and Makespan-Optimized Workload Scheduling in Cloud Computing
Authors: Lavanya V
Published: February 2025

In fact, it’s no longer a question of whether users should utilize computational resources from the cloud — the question is rather how to do it. Dynamic workload scheduling is however diffi-cult to optimize because of the interplay between energy consumption and makespan. Lastly, to overcome this, we put forward a reinforcement learning (RL) framework grounded on SARSA, with the objective of achieving that balance between makespan and energy consumption. In-dependently it adapts scheduling decision for tasks based on real time workload characteristics, without compromising the throughput but optimization the energy consumed. Through exper-iment, our proposed SARSA based scheduling algorithm has been show to improve over tradi-tional scheduling strategies and can potentially save a large amount of energy and minimize makespan. In this work, an adaptive mechanism is proposed that allows tuning of the cloud computing service to optimize its sustainability while minimizing a deleterious effect on the service quality.
Delay Tolerant Network Security: Enhanced Machine Learning Technique for Intrusion Detection System
Authors: Rajashri Chaudhari
Published: February 2025

Delay tolerant networks (DTNs) are intended for effective communication between nodes over huge distances and they are resourceful in extreme conditions. DTN stores and forwards messages when the participant node is in the range. Hence, there is no restriction on end-to-end connectivity and data is transferred over adaptable links between the nodes. Since the network functions on coordination among participant nodes, an untrusted node can affect the coordination. Thus, DTNs are vulnerable to different kinds of attacks influencing the performance of the network. The delay in the transfer of data and unstable connectivity of nodes depends on effective coordination while the possibility of misbehaviour by relay nodes increases network vulnerability to various types of network attacks like packet dropping attacks, flooded attacks, DoS attacks, gray hole attacks, and black hole attack disturbing the network connectivity. Denial of service attacks (DoS) is a major concern in DTN that adversely affects the network. Attacks on DTN can interrupt message delivery and degrade performance. The study proposes the detection of such attacks over DTN with an efficient machine learning (ML) algorithm. The delay tolerant network is a wireless network that transfers information among nodes and is monitored for malicious nodes using a pre-trained ML model. The voting technique is used to enhance the performance of detection. The network attacks are detected with significant accuracy and efficient secure communication is established in the network. Furthermore, the network simulator NS2 is employed to simulate the prevention of malicious attacks in the proposed system. This simulator offers a versatile and customizable environment for modelling various DTN scenarios and assessing the effectiveness of intrusion detection systems in a controlled setting. Our proposed model offers better performance than existing DTN security techniques.
Next-Generation Defect Detection in High Voltage Electrical Equipment via Deep Learning and Image Processing
Authors: Jaydeep Kale
Published: February 2025

The paper presents a deep-learning-based, thermal image-analysis advanced defect detection method for high-voltage electrical equipment. Based on the features extracted from the variant AlexNet of the convolutional neural network (CNN), the classification model designed with the classifier Random Forest (RF) achieved 94.8% accuracy. The photographs used for investigation were obtained on an infrared thermal camera at several substations in Chongqing, China. They were taken under cold weather conditions. The electrical components' thermal conditions were classified into two defective and non-defective types based on the temperature differential. The proposed approach supersedes other methods in terms of precision: the precision values are high at 93.2% and the recall values at 95.6%. The combination of CNN and RF forms a computationally efficient solution for achieving the enhancement of defect detection reliability in high-voltage systems. The results outline the potential of this technique in enhancing maintenance practices, minimizing equipment failure probabilities, and ensuring safe electrical infrastructure usage. The scope of future work will be on optimization techniques along with their application within some different environments.
Evolution of Wireless Communication: From 5G to 6G
Authors: Ravi Gatti
Published: February 2025

The evolution of wireless communication from 1G to the forthcoming 6G era marks an extraordinary journey of technological advancement and innovation. This paper offers a comprehensive overview of the imminent 6G revolution, set to redefine the wireless landscape with unparalleled speed, connectivity, and transformative applications. The key drivers behind 6G development, such as the increasing demand for high-speed data, ultra-low latency, and ubiquitous connectivity, are examined. Highlighted potential applications of 6G include immersive augmented reality, smart cities, and advanced healthcare, showcasing its potential to transform numerous aspects of society. In-depth discussions address challenges and opportunities related to spectrum utilization, energy efficiency, security, and privacy. The paper also explores foundational 6G technologies, such as terahertz frequencies and advanced antenna systems, underscoring the necessity for interdisciplinary collaboration among researchers, industry stakeholders, and policymakers. Additionally, it emphasizes the ethical and societal dimensions of 6G, advocating for digital inclusivity and equitable access to ensure the benefits of this technological leap are widely shared, promoting a holistic and responsible approach to its development..
Advanced CNN-Based Leaf Disease Detection for Sustainable Agriculture
Authors: Tanvi Deshmukh
Published: February 2025

Crop diseases are one of the major challenges to global agricultural productivity, and efficient and accurate solutions for early detection are needed. This paper proposes a CNN-based model for crop leaf disease detection, integrated with the Crop Pro user interface for practical deployment. The model is trained on an extensive dataset that contains 8,264 images of crop leaves belonging to various species, classified into 14 disease categories, with preprocessing and augmentation techniques further improving it. The CNN model had a training accuracy of 94.73% and a validation accuracy of 88.75%, showing robustness and the ability to generalize over different crops and conditions. Users can upload their leaf images to the Crop Pro platform that preprocesses and classifies images and uses them for high-confidence disease predictions. The availability of an iterative feedback loop helps improve the model based on input received from the user. Scalability of the system and application in real-world applications may offer promising solutions for precision agriculture through early intervention for sustainable crop management.
The Real Environment Impact of AI: Unveiling the Ecological Footprint of Artificial Intelligence
Authors: Beena Nawghare
Published: February 2025

Global environmental pollution has a devastating influence on the planet's population and jeopardizes humanity's future. The construction business is a major producer of waste and hazardous emissions into the atmosphere. It is vital to discover measures to reduce the damage done to nature. Currently, artificial intelligence technologies are one of the most promising approaches to helping the environment. This research investigates the use of green AI algorithms for measuring greenhouse gas (GHG) emissions in the context of ecological footprint assessment. Green AI algorithms prioritize sustainability and seek to lower AI systems' carbon footprints while monitoring GHG emissions data. These algorithms use environmentally conscious machine learning techniques to improve resource allocation, encourage energy-efficient model topologies, and prioritize renewable energy sources for AI model training. Carbon-aware optimization approaches are used to reduce the environmental impact of AI computations, resulting in a greener future. The incorporation of green algorithms into AI systems identifies the potential for emission reduction and energy efficiency, promoting environmentally beneficial behaviours across industries. The use of green algorithms allows for a full analysis of GHG emissions and ecological footprints, permitting a symbiotic interaction between technology and the environment for sustainable growth.
Cybersecurity in Network Traffic: Integrating Statistical Techniques with AI
Authors: Arun Kumar Chaudhary
Published: February 2025

This study investigates a hybrid model combining statistical components with AI models to support cybersecurity against attacks on network traffic. To overcome the restrictive nature of classical models such as signature based intrusion detection, this research integrates statistical methods like Z-scores and stack counting with AI techniques including logistic regression and neural network. The hybrid approach is recognized for its ability to identify anomalies, classify network traffic, and respond to changing cyber threats. Results from testing on synthesized data showed that the model was able to detect anomalies to a large extent, as there was considerable accuracy, precision, and recall in targeting malignant traffic. This unified pipeline connects traditional data analytical methods and machine learning in a manner that can expand to cater for real time cyber security needs.
Hybrid Learning Approach for Identifying Plant Characteristics and Diseases Through Image Analysis
Authors: Alok Singh Jadaun
Published: February 2025

Advancements in deep learning and computer vision have enabled significant progress in the automated analysis of plant images, aiding in the detection of diseases and characterization of plant traits. Combining these technologies into hybrid models offers the potential for enhanced accuracy and efficiency in addressing the complexities of plant disease identification in agricultural practices. Proposed research presents the development of a system based on deep hybrid learning for analyzing plant images, identifying various characteristics, and detecting diseases using computer vision techniques. The Plant Village dataset, comprising a diverse collection of plant images, was utilized to train and test the proposed models. The goal was to enhance the efficiency and accuracy of plant disease detection while creating a refined dataset to improve model performance in subsequent iterations.Two hybrid models were proposed to achieve superior results- Hybrid (DenseNet + SVM) and Hybrid (DenseNet + XGB). In both cases, the DenseNet architecture was employed for feature extraction, leveraging its deep layers to capture intricate details from the plant images. The extracted features were then passed through Support Vector Machines (SVM) and Extreme Gradient Boosting (XGB) classifiers, respectively, to classify the plant characteristics and identify diseases. The rationale behind selecting SVM and XGB was to exploit their ability to handle complex decision boundaries and high-dimensional data efficiently.The proposed models were evaluated extensively, achieving an accuracy of over 96% in both cases. The Hybrid (DenseNet + SVM) model demonstrated robustness in handling complex variations in plant characteristics and diseases, while the Hybrid (DenseNet + XGB) model excelled in terms of precision and computational efficiency. These hybrid approaches outperformed traditional deep learning models, providing improved classification performance and greater generalizability. The study emphasizes the creation of a new dataset using the extracted features and classification results, enabling further refinements in training and enhancing the accuracy of future models. The proposed hybrid learning framework demonstrates the potential to revolutionize plant disease detection, making it a valuable tool for researchers and practitioners in precision agriculture.
Leveraging Blockchain for Enhanced Data Integrity and Security in Information Systems
Authors: Rashmi Welekar
Published: February 2025

In the digital age, data accuracy and security are very important for making sure that information systems work well. When it comes to online risks and data breaches, old ways of doing things don't always work. This paper looks at how blockchain technology could be used to make information systems safer and more reliable by transforming data security and integrity. Blockchain is an autonomous record system that is known for being transparent and unchangeable. This makes it a great choice for managing data securely. The study looks at how blockchain can be added to current computer systems to make them less vulnerable and protect data better. First, we'll talk about the basic ideas behind blockchain technology, such as its structure and the cryptographic methods that make its security work. Different types of blockchains, like public, private, and group blockchains, and how they can be used in different kinds of information systems are also talked about. The study show how blockchain can be used in real life in areas like healthcare, banking, and supply chain management through a number of case studies. In each case study, the introduction of blockchain is linked to better security results, such as fewer attempts to access or change data without permission. The paper also talks about the problems that come with using blockchain, such as how to make it scalable, how much energy it uses, and how it needs to be used in a way that follows the rules. Based on our research, we can say that blockchain is a good way to improve data security and integrity, but it needs to be carefully integrated by taking into account system-specific needs and possible trade-offs. In the future, researchers should work on making blockchain setups work better and finding compatible solutions that can work with a wide range of information systems.
Evaluating the Impact of IoT Integration on Enterprise Information Systems Performance and Scalability
Authors: Amol D Gaikwad
Published: February 2025

Adding Internet of Things (IoT) technologies to business computer systems has changed how companies run their businesses, keep an eye on their assets, and use data to make decisions. IoT devices, like sensors, controllers, and linked systems, produce a lot of data that can be used to make business systems more scalable, improve performance, and simplify processes. The goal of this paper is to look at the pros and cons of companies adopting IoT technologies in order to figure out how integrating IoT technologies affects the performance and scale of corporate information systems. IoT integration offers big changes in how well business IS works by letting you analyze data in real time, automate tasks, and make operations run more smoothly. IoT-enabled systems can cut down on manual work, make better use of resources, and allow predictive maintenance by letting you watch and direct business processes in real time. Also, being able to gather and handle data from many connected devices can help you make better decisions, improve the customer experience, and run your business more efficiently. But while it's clear that integrating IoT has benefits, it also adds a lot of complexity, especially when it comes to making the system scalable. IoT devices are creating more, faster, and different kinds of data, which is making standard IS systems work harder. We need new ways to store, process, and send data. This study looks at how IoT affects the performance and scalability of business information systems (IS). It does this by studying current research, analyzing case studies, and figuring out the most important factors that affect how well IoT technologies are adopted. The study shows that Internet of Things (IoT) devices can make corporate information systems (IS) more scalable by making it easier to handle data, building better networks, and using more advanced computing models like edge computing and cloud computing.
An LSTM and Conventional, Global, and Object-Based Semantic Feature Fusion Framework for Indoor-Outdoor Scene Classification
Authors: Pandit T. Nagrale
Published: February 2025

This article proposes a novel approach that uses diverse features from a scene image using a variety of local, global, and object-based descriptors. The regional and image-based features are obtained using various conventional statistical descriptors, whereas a deep network VGG19 is used to extract the scene's global features. Scene objects based on specific features are concatenated to regional and global features after they are segmented using another deep network, YOLOV5m. While conventional features represent intensity-based characteristics, global and object features carry color depth details of the scene image. A long-term short memory (LSTM) network with a fully connected (FC) dense layer network is trained over images from four benchmark cross-datasets. Experimental evaluation over 5081 indoor-outdoor scene images showed that the proposed scene classification approach obtained 96% accuracy in identifying both categories on 15% test sample images.
Transformer Based Sentiment Analysis of Russia-Ukraine War using BERT Model
Authors: Deepak T. Mane
Published: February 2025

Sentiment analysis is an essential part of gaining deeper insights into particular tweets. Here, we analyse the inclinations that reflect user opinions and their sentiments regarding the tweets about the Russia and Ukraine war. Taking data from social media, we use sentiment analysis to gain insight into the wide range of public opinion. In this paper, we proposed customized Bidirectional Encoder Representations from transformers (BERT) natural language processing model to gauge the tone of the tweets about the war. A proposed model for sentiment analysis is fine- tuned on a dataset related to the Russia- Ukraine war. Proposed model to classify sentiments in domain specific texts to ensure accurate and context aware sentiment predictions. Our unique approach leverages BERTs contextual language understanding model to study Russia- Ukraine relations. The model achieved an accuracy of 94.73% on the fitted dataset. Everyone benefits from this research, and the public as it gives them a better understanding of public opinion. Experiment results shows that the performance of the proposed model is better than the existing approaches.
Dynamic Question Paper Generation using Bloom and NLP based Heterogeneous Feature Extraction and Machine Learning Techniques
Authors: Kanchan Babaji Dhomse
Published: February 2025

Automatic question generation is a very difficult task in the field of Natural Language Processing (NLP) and Machine Learning (ML). The procedure requires bidirectional language processing, including both Natural Language Understanding to comprehend the input text and Natural Language Generation to produce queries in textual form. This article presents our system for generating factual inquiries from unstructured English text. The strategy used combines conventional linguistic methodologies that rely on sentence structures with several machine learning techniques. Initially, we extract lexical, syntactic, and semantic data from the input text. Subsequently, we create a hierarchical collection of patterns for each phrase. The characteristics are derived from the patterns and then used for the automatic acquisition of new transformation rules. The learning technique we use is entirely data-driven, since the transformation rules are derived from a collection of initial sentence-question pairings. The benefits of this technique are twofold. Firstly, it allows for the straightforward addition of additional transformation rules, enabling the generation of many sorts of questions. Secondly, the system may be continuously enhanced via bloom algorithm with collaboration of NLP and machine learning. The framework furthermore has a question assessment module that assesses the calibre of created questions. It functions as a filter to discern the most optimal inquiries while removing erroneous or duplicate ones. We conducted many tests to assess the accuracy of the produced questions and also compared our system with other cutting-edge technologies. Our findings recommend that the generated queries surpass the performance of existing algorithms and are on par with questions produced by people. In addition, we have developed and released an interface that includes all the datasets we have produced and the questions we have analysed.
Enhancing Non-Invasive CML Diagnosis: High-Sensitivity Biosensors for Liquid Biopsy using Microfluidic chips
Authors: Tanya Arora
Published: February 2025

The BCR-ABL1 genetic fusion is the driving force in patients with myeloproliferative disorders having Chronic Myeloid Leukemia (CML), a type of cancer. Traditionally, diagnosis has traditionally been made by various invasive procedures such as bone marrow biopsy, which is painful, risky, and requires well-equipped health facilities; thus it is less accessible in resource-limited settings. Liquid biopsies can make use of peripheral blood samples, so it is possible to dispense with painful biopsies. Besides, biosensors have been designed capable of detecting CML biomarkers at a sensitive and specific level in blood samples. High sensitivity biosensor-integrated microfluidic chip technology for liquid biopsy-based diagnosis of CML is discussed in this research study. This can involve the detection of biomarkers at very low sample requirements with high diagnostic accuracy by combining microfluidic chips with biosensors. It might have an application in rapid, non-invasive, and accurate diagnosis of CML. It propels cancer diagnostics beyond former dependency on traditional biopsy procedures. In this regard, it may alter the diagnostic procedures of patients with CML because it presents an option which is less invasive, thereby availing the prospects of early diagnosis to a wider population segment.
AI-Based Applications Enhancing Computer Science Teaching in Higher Education
Authors: Lydia Velázquez-García
Published: February 2025

The incorporation of Artificial Intelligence (AI) in higher education has gained significant attention as it presents new opportunities to improve the teaching and learning process. This paper aims to analyze how AI applications can support the teaching and learning of computer science in higher education. By reviewing various scientific publications, this paper offers an in-depth analysis of how AI-driven tools and applications have been successfully integrated into computer science courses. Key AI applications considered include intelligent tutoring systems, assessment, performance prediction, academic management, educational innovation, and adaptive learning. These tools have been shown to increase student engagement, provide tailored instruction, offer timely feedback, and enable the scalability of high-quality education. Also, the paper addresses the challenges associated with AI in education, such as diversity of educational contexts, security and data privacy, algorithmic bias, and the importance of faculty preparation. This article emphasizes the transformative potential of AI to enhance computer science education in the context of higher education and identifies mechanisms for research and practice to take full advantage of AI capabilities in designing effective and inclusive learning environments, guided by a comprehensive synthesis of current research and case studies.
A Hybrid Ensemble Framework for Intrusion Detection in IoT Using Blockchain-Integrated Fog Networks
Authors: Pratibha Sharma
Published: February 2025

The exponential growth of IoT has become a source of concern regarding cybersecurity vulnerabilities in real time and distributed environments, the research proposes a novel Fog computing-based hybrid ensemble framework using Blockchain technology to enhance IoT network security. It proposes advanced data preprocessing, feature selection, and hybrid ensemble learning that leads to remarkable performance: 99.5% accuracy, 99.2% precision, 99.4% recall, and a false positive rate of just 0.05% on the UNSW-NB15 dataset. Blockchain integration ensures secure and immutable logging of detected threats, further enhancing trust in the system. The scalability and robustness of the proposed framework are demonstrated in its ability to process high-traffic IoT networks while guaranteeing optimal resource efficiency. These results make the proposed approach a state-of-the-art solution for real-time attack detection in IoT networks, which can meet modern challenges in cybersecurity.
Relay Selection Technique for Energy-Efficient Data Transmission Policy for UAV-Assisted LoRaWAN
Authors: J.Vijaya Barathy
Published: February 2025

An unmanned aerial vehicle (UAV), is an aircraft that does not have a human pilot and is controlled either independently by the aircraft's computers or by remote control. Because of their high mobility, FANETs are often used in UAVs because they can disrupt communications as well as network stability due to frequent topology changes. In-depth exploration and implementation of UAV network development, concentrating on diverse topologies such as star, multi-star, and network configurations, we developed the Energy-Efficient Data Transmission Policy for UAV (EEDTP-UAV) model. It emphasizes mobility, energy efficiency, UAV distance control, and road availability in the context of Flying Ad-hoc Network (FANET) technology development. Special attention is given to path optimization techniques and different link types, which are crucial for effective UAV communication. By using a relay-based energy-efficient process, off-grid user connections are optimized according to energy consumption meters, and data is collected cooperatively between the UAV cluster and the mobile synchronous node, providing equal energy usage and efficient data transmission. Due to its flexibility and adaptive nature, the proposed algorithm is well-suited for real-time UAV swarm operations that tackle dynamic path planning, and energy efficient communication methods such as airborne navigation or crowd control. Compared with existing model EEDS, DSSPCA, EEUCH, and ESRD-PDCA models with our proposed EEDTP-UAV model. By the following parameters communication delay, energy efficiency, data success rate, throughput, and routing overhead have been calculated.
Integrating Neural Networks for Enhanced Detection and Classification of Retinal Diseases in OCT Images
Authors: K. Sathish
Published: February 2025

With the use of neural networks, and more especially a hybrid model combining Convolutional Neural Networks (CNN) and Autoencoders (AE), this study introduces a new strategy for improving the identification and categorization of ocular disorders in Optical Coherence Tomography (OCT) pictures. Early and precise diagnosis is crucial for treating retinal illnesses such diabetic retinopathy as well as age-related macular degeneration, which are among the most common causes of visual impairment. Our goal hybrid model draws on the strengths of both CNNs and AE to enhance the precision and stability of illness identification and categorization by extracting features and performing reducing dimensionality and denoising, respectively. In this approach, OCT images are first processed through the CNN component to extract relevant features capturing intricate retinal structures and pathological characteristics. Subsequently, the AE component reduces the dimensionality of the extracted features while enhancing their clarity by mitigating noise and irrelevant information. The integrated features from both CNN and AE are then utilized to classify retinal diseases, providing a more discriminative and informative representation. The suggested hybrid model is shown to be better to traditional techniques by experimental findings on a broad collection of OCT images. When it comes to detecting and classifying retinal illnesses, the hybrid neural network shows increased specificity as well as sensitivity, which helps physicians make more precise diagnosis faster. Experiment is carried in python and the proposed model outperforms all others with an outstanding accuracy of 98.41%. It excels in sensitivity (97.14%) and specificity (98.12%), affirming its remarkable precision in identifying retinal diseases. The precision and F1 score for the suggested model are 97.03% and 97.56%, respectively, signifying its exceptional capacity for accurate disease classification.