Volume-13 , Issue-2 , Feb -2025, E-ISSN: 2347-2693

Automatic diagnosis and control of wheat plant disease are highly desired by agricultural experts. Accurate diagnosis of wheat leaf diseases is important for effective crop management. This study introduces a Wheat Leaf Convolutional (WLC) model, an enhancement of the VGG16 architecture, designed to detect and classify six distinct types of wheat leaf diseases using deep learning techniques. The model is trained using wheat leaf images dataset, augmented by Generative Adversarial Networks (GANs) to improve generalization. The WLC model got an accuracy of 94.88%, outperforming classical CNN models such as ResNet-50, AlexNet, and MobileNet by significant margins. Key metrics, including recall, precesion and F1-score, were evaluated across six disease categories: Leaf Rust, Black Chaff, Powdery Mildew, Wheat Streak, Septoria, and Healthy plants. Experimental results show that the WLC model accurately and efficiently identifies diseases, making it a useful tool for real-time applications in precision agriculture. This work contributes to improving wheat disease diagnosis, enabling timely interventions and better crop management practices.

Wheat diseases, Image classification, Deep learning, Precision agriculture, Convolutional neural networks, Generative Adversial Networks, Data Augmentation

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Girish Saunshi, Ramesh Badiger, Rajesh Yakkundimath, Shridhar Chini, "A Novel Wheat Leaf Disease Classifier Leveraging Generative Adversarial Networks," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.1-6, 2025.

AI and ML are redefining predictive maintenance for vehicle fleets to boost uptime and cut expenses. We evaluate key academic and industry examples of predictive maintenance`s uses, challenges, and potential advances. Start with traditional maintenance problems and AI/ML disruption. Next, we`ll discuss predictive maintenance`s history, goals, issues with conventional methods, and the shift to proactive measures. Studies on bus fleets, oil and gas operations, and engine reliability show AI-driven predictive maintenance`s effectiveness. In view of AI`s importance in fleet management, the essay examines data collection, preprocessing, and predictive maintenance ML algorithms. Case studies and real-world implementations demonstrate these technologies` successes, failures, and lessons. AI and quantum breakthroughs in electric cars and hidden patterns in heavy vehicle maintenance data create a holistic view of various sectors` uses and issues. Analysis of how proactive maintenance scheduling, condition-based monitoring, and predictive analytics improve dependability and downtime. A bus fleet and oil and gas production study found that AI-driven solutions improve fleet reliability. AI-driven predictive maintenance`s ROI and financial benefits show its value. Case studies on automobile engine dependability and AI cost implications demonstrate these technologies` advanced financial benefits. Future predictive maintenance technologies and trends are discussed last. It highlights how edge, IoT, 5G, digital twins, and quantum computing may improve preventative maintenance. Strategic planning, cybersecurity, and workforce skill development are prioritized, but the changing landscape brings challenges and opportunities. This study extensively examines AI and ML-based car fleet predictive maintenance. It acknowledges predictive maintenance`s future potential and limitations. It shows how these technologies may transform reliability, downtime, and costs using data from several enterprises.

Predictive Maintenance, Artificial Intelligence (AI), Machine Learning (ML), Fleet Management, Cost Savings, Reliability, Data Analytics, Digital Twins, Quantum Computing, Edge Computing

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Dinesh Eswararaj, Lakshmana Rao Koppada, Ram Sekhar Bodala, "Quantifying the Influence of Artificial Intelligence and Machine Learning in Predictive Maintenance for Vehicle Fleets and Its Impact on Reliability and Cost Savings," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.7-15, 2025.

Email spam remains a persistent challenge, with cybercriminals constantly evolving tactics to bypass traditional detection methods. In response, this research introduces a novel two-stage classification model that combines the strengths of logistic regression, principal component analysis (PCA), and a feedforward neural network to achieve exceptional spam detection performance. The first stage employs a rapid logistic regression classifier to filter out obvious spam emails, dramatically reducing computational overhead. We then subject the remaining emails to Principal Component Analysis (PCA), extracting the most salient features while minimizing noise and dimensionality. This transformed feature space is then fed into a neural network, empowering it to capture the complex, non-linear patterns indicative of sophisticated spam attacks. Evaluation of the widely-used SpamAssassin Public Corpus and Lingspam datasets demonstrated the synergistic benefits of this hybrid approach, achieving 98.0% accuracy in spam detection for the Spam Assassin Public Corpus, which was refined from an initial accuracy of 99.95% following further testing and optimization, and 99.34% accuracy for the Lingspam dataset respectively, in spam detection. The strategic combination of techniques transcends the traditional speed-accuracy tradeoff, simultaneously creating a new benchmark in both performance metrics. This robustness, consistency, and scalability make the proposed model a practical and effective solution for real-world spam filtering, with significant implications for securing email communication and protecting users from cybercrime.

Email Spam Detection, Hybrid Classification Model, Logistic Regression, Principal Component Analysis, Feedforward Neural Network, Cybersecurity in Email Communication

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May Stow, Bolou-ebi Samuel Ezonfa, "Two-Stage Email Classification Model for Enhanced Spam Filtering Through Feature Transformation and Iterative Learning," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.16-27, 2025.

Air pollution presents substantial risks to public health and environmental sustainability, necessitating robust predictive models capable of monitoring and forecasting air quality. This study aimed to design and evaluate a robust air pollution prediction model by leveraging data-driven modeling techniques. The research employed a comprehensive methodology that involved the aggregation of global air pollution datasets, followed by data preprocessing and transformation to ensure the accuracy and relevance of the input data. This data-driven approach facilitated the analysis and interpretation of the dataset using various machine learning algorithms. The study explored the performance of several machine learning algorithms, including AdaBoost, Decision Tree, Extra Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), and Neural Network, to determine their effectiveness in predicting different levels of air quality. Each algorithm was evaluated based on precision, recall, f1-score, and overall accuracy, with a particular focus on challenging air quality categories such as "Unhealthy" and "Very Unhealthy." The results revealed that while some models like Decision Tree, Extra Tree, Random Forest, and Neural Network achieved high accuracy and f1-scores, others such as AdaBoost and Naïve Bayes displayed limitations in handling certain air quality categories. To overcome these limitations and enhance the overall prediction accuracy, an ensemble model was developed by combining the strengths of the top-performing algorithms. The ensemble model demonstrated exceptional performance, achieving perfect precision, recall, f1-scores, and accuracy across all air quality categories, indicating its potential as a highly reliable tool for real-time air quality monitoring and prediction. This study concludes that the ensemble model represents a significant advancement in air pollution prediction. Hence, offering an efficient solution for environmental monitoring systems. The study highlights the importance of integrating multiple machine learning algorithms to improve model robustness and accuracy, providing valuable insights for public health management and policymaking. The study recommends further exploration of ensemble models in different geographic regions and the integration of real-time data from IoT devices to enhance the model`s applicability and effectiveness in diverse environmental scenarios.

Air Pollution Prediction, Machine Learning, Ensemble Model, Environmental Monitoring, Data-Driven Modeling, Air Quality Forecasting

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Ebikombo-ere Olodiama, Ogheneromesuo Afisi, Moses Peter, "An Ensemble Machine Learning Approach for Accurate Air Pollution Prediction and Environmental Monitoring," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.28-38, 2025.

A novel Gray-Scale Thresholding Method [GSTM] for segmenting the region of interest and a Deep Convolutional Neural network model for the apple fruit sorting system has been proposed in this paper. First, the GSTM method converts the acquired colour image into a grayscale image and calculates the threshold value using the Gray pixel values. The acquired colour apple image was then segmented using the calculated threshold value to extract the diseased/defective part alone for further processing. Second, a Deep Convolutional Networking model was designed to classify the apple images as sound or diseased/defective apple images. The result obtained using the GSTM was compared with similar Grayscale thresholding methods like Otsu and Kapur. It was found that GSTM’s execution time was less and the visual segmentation was good compared to the other two methods in extracting the diseased/defective area. The Deep Convolutional Network using GSTM segmented images gave a classification/sorting accuracy rate of 91.67%.

Thresholding Algorithm, Region of Interest, Classification, Deep Neural Network, Apple Fruit Sorter

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[30] Domo Omatsogunwa Ereku, Vincent I.E. Anireh, Onate Egerton Taylor, "Model For Email Spam Classification Using Hybrid Machine Learning Technique”, International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.24-32, 2025.

M. Henila, "Diseased Area Segmentation Using a Novel Gray-Scale Thresholding Algorithm and Classification Using a New Deep CNN Model for Apple Fruit Sorting," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.39-48, 2025.

This literature review examines the roles of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in identifying and interpreting online threats. As AI and ML technologies advance, their use in analyzing vast online data for potential threats has grown significantly. The review systematically evaluates current methodologies for detecting and assessing threats, particularly in social media and online forums, which are both information hubs and sources of harmful content. Key findings highlight the effectiveness of BERT-based models in hate speech detection across languages and platforms, emphasizing their interpretability and transparency advantages over traditional neural networks. Models like GPT-4 further expand threat identification capabilities, detecting cyber threats and abusive language, with implications for public safety and mental health monitoring. Challenges remain, particularly in handling noisy, diverse, and imbalanced social media datasets. Domain-specific word embeddings and ensemble techniques, such as combining BERT with TextCNN and BiLSTM, show promise in improving detection accuracy in complex data environments. The review advocates for continued focus on hybrid and ensemble models to address data complexities and calls for future research to enhance model transparency and address ethical concerns like data privacy. Given the rise in digital communication, real-time threat detection is crucial for public safety, national security, and violence prevention. This review consolidates findings on the efficacy of AI and ML in detecting online threats, identifies recurring challenges, and outlines research gaps to guide future advancements. By synthesizing recent studies, it provides a structured analysis of the current capabilities and limitations of AI and ML in online threat monitoring, contributing to a foundational understanding of how these technologies can evolve to enhance societal safety.

Artificial Intelligence (AI), Online Threat Detection, Machine Learning (ML), Large Language Models (LLMs)

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S. Shamroukh, T. Johnson, "Leveraging Artificial Intelligence and Machine Learning in Online Threat Detection," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.49-56, 2025.

Cloud computing is a more current approach to managing long-term business needs. Cloud computing is conceivable thanks to the rapid advancements of the web and next-generation laptops. Cloud computing supports private businesses in several ways. Backs up and stays updated with company information, runs schedulers, and works on data sharing. Small organization owners need to get what they need in today`s ever-changing business environment, whether on their PCs, tablets, or phones, in the workplace, in the field, or movement. Cloud computing allows customers to access information from any area with a web connection. Cloud logging targets private companies by leveling the playing field. It will enable them to access trending innovations used by large companies without significant out-of-pocket expenses. Independent businesses can store information, run applications, and train teams remotely, supporting productivity and eliminating IT costs. The benefits of the Cloud also offer adaptability, so organizations can change their resources according to their interests without putting too much effort into the foundations. Additionally, cloud phases provide information security and hardening, safeguarding critical business data. By adopting cloud computing, private companies gain adaptability, cost reserve funds, and greater efficiency, making them innovative enterprises for development. This paper will discuss how the Cloud has affected the small business industries.

Cloud Computing, Small Businesses, Cloud Technologies, Remote Servers, AWS, SLAs

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Hasini Koka, "Enhancing Agility and Security for Small and Medium Businesses through Cloud Technologies," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.57-63, 2025.

Data privacy is a critical challenge in publicly shared datasets. This study investigates the impact of privacy-preserving techniques, including gaussian noise distribution and k-anonymity-based generalization adjusting ?, on data utility. Using a dataset related to stress prediction, we apply these techniques to safeguard sensitive attributes while assessing their impact on machine learning models. Logistic Regression, Random Forest, and k-Nearest Neighbours (KNN) are used to evaluate utility preservation. Our results highlight the trade-off between privacy and predictive performance, demonstrating that k-anonymity generalization maintains better model accuracy compared to noise addition. These findings contribute to privacy-aware machine learning, applicable to domains handling sensitive demographic and financial data.

Privacy-Preserving, Machine Learning, Noise, K-Anonymity, Data Utility

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N. Charuhasini, P. Drakshayani, P. Dhana Sri Aparna, P. Pravalika, Ch. Praneeth, "Analysing Privacy-Preserving Techniques in Machine Learning for Data Utility," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.64-70, 2025.

The main focus of this project is the creation of an Inventory Management System (IMS), conceived to improve the inventory management of businesses of all sizes. It includes key features such as stock level tracking in real-time, automatic reorder notification generation, and detailed reporting tools. The IMS, with a friendly interface, allows users to efficiently manage inventories at multiple locations and streamline order processing. The technologies utilized for ensuring data entry accuracy and enhancing accessibility are barcode scanning and cloud computing. This project demonstrates IMS`s capabilities to change inventory management practices by offering savings and reducing turnaround time, which improves customer satisfaction, as elaborated in the paper. The author ends with views on the implementation and future improvement for scalability and integration with other business systems. This paper presents a system design and implementation aimed at enhancing the efficiency of inventory tracking, sales management, and financial transactions. The system encompasses customer information management, real-time stock updates, sales tracking, and payment management. It provides a seamless experience to customers and administrators alike. The system minimizes errors and ensures accurate data flow and decision-making based on detailed reports and automated alerts with an interlinked database for customer, product, sales, and payment information system. The expected benefits include increased operational efficiency, improved inventory management, streamlined transaction processes, thus improving customer satisfaction and effective use of business resources.

An application for inventory management; track stock, collect real-time data, and automate with cloud computing; supply chain management; order fulfillment; data analytics; user-friendly interface; operational efficiency; cost savings; customer satisfaction

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Prashant Bhandare, Harshvrdhan Sirsat, Om Lokhande, "Shrinath Automobiles Shop: An Application for Inventory Management System," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.71-77, 2025.

Cloud Enterprise Resource Planning (ERP) systems are crucial in streamlining business processes, integrating various functions, and improving organizational efficiency. This paper presents a comprehensive process guide for ERP implementation, detailing each phase from planning and selection to deployment and post-implementation support. Additionally, it addresses the common challenges organizations face during ERP implementation, including data migration, user adoption, system customization, and change management issues. By exploring these challenges and providing actionable insights, this paper aims to guide businesses with the knowledge to navigate the complexities of ERP implementation successfully. Through a combination of practical steps and real-world examples, this guide is a valuable resource for organizations seeking to optimize their ERP systems and ensure long-term success.

Enterprise Resource Planning, Business Process Integration, Data Migration, User Adoption, System Customization, Cloud Technology, Change Management, Best Practices, and Implementation Support

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Uma Maheswara Rao Ulisi, "A Comprehensive Process Guide to ERP Implementation and Its Challenges," International Journal of Computer Sciences and Engineering, Vol.13, Issue.2, pp.78-85, 2025.