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2025 International Conference on Pervasive computational Technologies, ICPCT 2025

作者： Suneetha, Irala Prathima, Ch. Sirisala, Srinivasulu Mahalakshmi, S.S.D.K. Balaji Nanda Kumar Reddy, K. Sambana, Bosubabu Annamacharya Institute of Technology and Sciences Ece Department Tirupati India Mohan Babu University Dept. of Ds Tirupati India Cvr College of Engineering Department of Cse Telagana India Lendi Institute of Engineering and Technology Department of Cse Vizianagaram India Annamacharya Institute of Technology and Sciences Eee Department Tirupati India Mohan Babu University School of Computing Department of Data Science Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331508685

Vehicle detection and counting using OpenCV is a crucial aspect of modern traffic management systems. This mini project aims to develop a robust solution for detecting vehicles in real-time video streams and accurately counting them as they pass through a designated area. OpenCV, a popular computer vision library, is utilized for its extensive functionality and ease of implementation. First, the video stream is captured and processed to extract individual frames. Subsequently, each frame undergoes preprocessing techniques such as resizing, grayscale conversion, and noise reduction to enhance vehicle detection accuracy. Vehicle detection is achieved through the implementation of a deep learning- based object detection algorithm, such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). These algorithms are trained on large datasets to recognize vehicles with high precision. Once vehicles are detected, a tracking algorithm is employed to maintain continuity between frames and accurately count the number of vehicles passing through the specified region of interest. This tracking algorithm ensures that vehicles are not double-counted and provides a reliable count for traffic analysis purposes. © 2025 IEEE.

关键词： Noise abatement

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data-Driven Assessment of Complexity-Induced Risks in Infrastructure Projects

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Journal of Construction engineering and Management 2025年第7期151卷

作者： Moussa, Ahmed Ezzeldin, Mohamed El-Dakhakhni, Wael Dept. of Civil Engineering McMaster Univ. 1280 Main St. West HamiltonONL8S 4L7 Canada Dept. of Civil Engineering School of Computational Science and Engineering McMaster Univ. 1280 Main St. West HamiltonONL8S 4L7 Canada

Infrastructure projects are characterized by inherent complexities that often lead to their poor performance. Notwithstanding challenges posed by various risks and their interactions, the additional non-linear and dynamic interdependence-induced complexities make infrastructure projects susceptible to systemic risks - probable component disruption that can lead to cascade (system-level) disruptions. The study of teams/resource interdependence-induced systemic risks in an environment of interacting risks is scarce in the literature. In addition, several previous studies demonstrated that current risk interactions and systemic risk analysis models are impractical due to their complexity and limited theoretical application domains. In this respect, the current study fills this knowledge gap by developing a data-driven risk interactions and systemic risk management approach. This approach is formulated in three stages: (1) quantifying risk interactions and teams/resources interdependence;(2) building machine learning model (ML) models to predict project performance based on the quantified characteristics;and (3) devising relevant mitigation strategies. The study also includes a practical demonstration application of the approach to present a step-by-step demonstration for each stage - thus guiding practitioners to proactively safeguard against risk interactions and systemic risks. The current work contributes to the body of knowledge by laying out the foundations of investigating the compound phenomenon of risk interactions and systemic risks as well as by presenting an effective approach to achieve that endeavor. Overall, the current study introduces a reliable and practical approach to enhance the performance of infrastructure projects through interacting risks- and systemic risk-informed management strategies. © 2025 American Society of Civil Engineers.

关键词： Risk assessment

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SkyNetPredictor: Network Performance Prediction in Avionic Communication using AI

arXiv

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arXiv 2025年

作者： Mukhtar, Hind Schaub, Raymond Erol-Kantarci, Melike Satcom Direct Inc. Melbourne Florida University of Ottawa OttawaON Canada Dept. of Data Science Satcom Direct Inc. MelbourneFL United States Dept. of Electrical & Computer Engineering University of Ottawa OttawaON Canada

Satellite-based communication systems are integral to delivering high-speed data services in aviation, particularly for business aviation operations requiring global connectivity. These systems, however, are challenged by a multitude of interdependent factors such as satellite handovers, congestion, flight maneuvers and seasonal trends, making network performance prediction a complex task. No established methodologies currently exist for network performance prediction in avionic communication systems. This paper addresses the gap by proposing machine learning (ML)-based approaches for pre-flight network performance predictions. The proposed models predict performance along a given flight path, taking as input positional and network-related information and outputting the predicted performance for each position. In business aviation, flight crews typically have multiple flight plans to choose from for each city pair, allowing them to select the most optimal option. This approach enables proactive decision-making, such as selecting optimal flight paths prior to departure. © 2025, CC BY-NC-ND.

关键词： Satellite communication systems

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset

Comparative Analysis of Machine Learning Techniques for Arab...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Hani Attar Yousef Sharrab Saja Smadi Safa Jbaily Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Data Science and Artificial Intelligence Isra University Amman Jordan dept. Computer Science Yarmouk University Irbid Jordan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency; indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Text categorization Artificial neural networks Nearest neighbor methods Natural language processing Robots Sports

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Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

arXiv

引用

arXiv 2025年

作者： Taha, Ahmed M. Aly, Salah A. Darwish, Mohamed F. Dept. of CE Faculty of Engineering Egypt University of Informatics Cairo Egypt Faculty of Computing and Data Science Badya University Giza Egypt Faculty of Science Fayoum University CS&Math Branch Fayoum Egypt Dept. of Pathology Faculty of Medicine Badya University Giza Egypt

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuraies;and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics. © 2025, CC0.

关键词： Magnetic resonance imaging

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Deep Learning Driven - Pest Classification and Sustainable Agriculture

Deep Learning Driven - Pest Classification and Sustainable A...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Sirish Kumar R Usha B Rama Kantha Reddy S Ramachandra P Jaya Prakash B Swetha Dept. of Data Science School of Computing Mohan Babu University India CSE Madanapalle Institute of Technology and Science Madanapalle CSE (AI and ML) Sri Venkateswara College of Engineering Tirupati CSE Mother Theresa Institute of Engineering and Technology Palamaner Department of IT Sri Venkateswara College of Engineering Tirupati Dept. of Master of Computer Applications Mohan Babu University India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices.

关键词： Deep learning Accuracy Insects System performance Transfer learning Crops Pesticides Agriculture Convolutional neural networks Testing

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Development of an Intelligent System for Predictive Analysis of Student Academic Performance Using Machine Learning Techniques

Development of an Intelligent System for Predictive Analysis...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anand Tripathi KTV Reddy Dept. of Artificial Intelligence and Data Science Datta Meghe Institute Of Higher Education and Research Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This research proposes an intelligent system utilizing machine learning techniques to predict student academic performance. A Random Forest classification model was developed based on a dataset consisting of key attributes such as attendance, previous grades, hours spent weekly on studying, medical emergencies, and assignment scores. The model classifies students into three categories: High-Performing, Average, and At-Risk, achieving an impressive accuracy of 99 %. Attendance and previous grades emerged as the key determinants of academic success. To facilitate real-world application, this project also includes an interactive Streamlit-based web application, enabling educators to make instant data-driven predictions by inputting students' academic details. This tool empowers educators with actionable insights, allowing them to implement timely interventions, optimize resource allocation, and adapt learning experiences for improved student outcomes. The study underscores the rapid advancements achieved through predictive analytics in education. By integrating artificial intelligence into pedagogic practice, institutions can proactively address learning difficulties, enhance student engagement, and foster academic excellence.

关键词： Education data visualization Learning (artificial intelligence) Alarm systems Real-time systems Resource management data mining Predictive analytics Intelligent systems Random forests

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Drug Recommendation System Using Patient Reviews Based on Sentimental Analysis

Drug Recommendation System Using Patient Reviews Based on Se...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： C Siva Kumar P.Lakshmi Sagar Golla Dinesh Kumar Chereddy Harshitha Reddy Bandlamudi Sai Krushna Chikkem Mahidhar Reddy Dept. of CSSE Sree Vidyanikethan Engineering College Tirupati A.P India Department of Data Science Sri Venkateswara College of Engineering (Autonomous) Tirupati A.P India Sree Vidyanikethan Engineering College Tirupati A.P India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The integration of digital technology in healthcare has been greatly facilitated by the internet. It has made access to vital data quite easy, with which solutions created help in improving patient outcomes. This leads to an improvement in patient care and medical procedures. Vigilance is required to avoid the dissemination of information that may be incorrect or dangerous, and thus harm patients and healthcare providers alike. The amount of patient-generated data involved is huge, and this has become quite central to the drug recommendation system in the healthcare industry. It is envisioned that medication recommendations by these systems are highly precise to increase efficacy and accuracy of treatment. Using Natural Language Processing tools, the suggested approach will make a prediction about the best drug, using ratings and reviews made by the patients. It would be advisable to apply some NLP approaches to the input provided by the patient to extract some useful insights and information that might help in prescription decisions. Examples of some machine learning methods applied for enhancing accuracy and dependability of such drug recommendations include the XGBoost classifier and LightGBM model. These algorithms further process and analyse data to bring out the relationships and patterns not otherwise evident. They therefore provide crucial help toward the formation and establishment of an effective or reliable drug-related recommendation system and greatly improved the treatment of patients.

关键词： Drugs Industries Accuracy Machine learning algorithms Reviews Medical services Machine learning Natural language processing Reliability Recommender systems

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DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO

arXiv

引用

arXiv 2025年

作者： Naidu, Aditya Prashant Gosalia, Hem Gakhar, Ishaan Rathore, Shaurya Singh Didwania, Krish Verma, Ujjwal Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

Although advances in deep learning and aerial surveillance technology are improving wildlife conservation efforts, complex and erratic environmental conditions still pose a problem, requiring innovative solutions for cost-effective small animal detection. This work introduces DEAL-YOLO, a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images by using multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box, ensuring smoother localization and reducing abrupt deviations. Additionally, the model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency. The Scaled Sequence Feature Fusion (SSFF) module enhances object detection by effectively capturing inter-scale relationships, improving feature representation, and boosting metrics through optimized multiscale fusion. Comparison with baseline models reveals high efficacy with up to 69.5% fewer parameters compared to vanilla Yolov8-N, highlighting the robustness of the proposed modifications. Through this approach, our paper aims to facilitate the detection of endangered species, animal population analysis, habitat monitoring, biodiversity research, and various other applications that enrich wildlife conservation efforts. DEAL-YOLO employs a two-stage inference paradigm for object detection, refining selected regions to improve localization and confidence. This approach enhances performance, especially for small instances with low objectness scores. © 2025, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Design and Implementation of a Comprehensive RAG-Driven Dashboard Within the ILO System for data Visualization and Query Support: A Case Study of the University of Petra

Design and Implementation of a Comprehensive RAG-Driven Dash...

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computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu-Arqoub Abed Alkarim Banna Nuha El-Khalili Mohammad Al-Shaikh Hasan Department of Computer Science University of Petra Amman Jordan Data Science & Artificial Intelligence Dept. University of Petra Amman Jordan Software Engineering Department University of Petra Amman Jordan Computer Science Department Brunel University London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This article presents a comprehensive study on designing and implementing a dashboard for enhanced data visualization and query support at the University of Petra. The system leverages retrieval-augmented generation (RAG) and large language models (LLMs) to support diverse document types, including curricula, course descriptions, and program outcomes, alongside intended learning outcome (ILO)-related files. Our implementation demonstrates significant improvements in data accessibility and query response times while maintaining high accuracy in information retrieval and visualization. Through extensive evaluation, we show that this innovative approach transforms data management processes in higher education by enabling natural language interactions with educational data systems, building upon established business intelligence frameworks while introducing advanced AI capabilities.

关键词： Large language models Retrieval augmented generation Natural languages data visualization Transforms Educational technology Information retrieval data systems Business intelligence Time factors

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