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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture

Multispectral Images and IoT Based Tea Plantation Monitoring...

引用

Advanced Research in Computing (ICARC), International Conference on

作者： K. P. W. D. V. Kariyawasam P. N. Kahandagamage M. R. R. Fernando J. M. Fernandopulle Vishan Jayasinghearachchi Kapila Dissanayaka Dept. of Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Computer Science Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.

关键词： Scalability Plantations Systems architecture Crops Real-time systems Internet of Things Resource management Reliability Monitoring Stress

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Laser-assisted Multidimensional Imaging Modalities for Early Detection of Tumours in Bio Tissues: An In Vitro Study

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Lasers in engineering 2025年第4-6期59卷 199-215页

作者： Akhtar, M. Ullah, H. Aslam, K. Siddique, M.A. Akram, A. Majid, M.A. Abdullah-Al-Wadud, M. Aleem, A. Farhan, M. Batool, Z. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

The idea, upon which this research work depends, is the early detection of tumours in different biological soft tissues like bladder, respiratory and thigh tissues. Imaging and spectroscopic technique that were used to study healthy and cancerous tissues were white light microscopy and Fourier-transform infrared radiation (FTIR) spectroscopy. The observation and calculations collected through all these imaging techniques prove as helping tools to differentiate healthy and cancerous body tissues. Soft tissues from patients who had been diagnosed with bladder, respiratory, and thigh cancer were gathered from the Bahawalpur Victoria Hospital, Pakistan while keeping in mind the ethical research perspective. Two-dimensional (2-D) white light microscopy images showed that healthy and cancerous tissues have a different kind of shapes, sizes and structure arrangements. Normal tissues images show a regular pattern of structure arrangement, but cancerous tissues show an irregular shapes, sizes, and structures. Similarly, FTIR spectroscopy graphs represents different peaks of stretching vibrations of C-H, O-H, C-C, N-H, CH2 double and triple bonding of different biological tissues like bladder, respiratory and thigh tissues. This research work will help in the medical field for early detection of tumour which helps to diagnose the tumour and also helpful for better treatment planning in early stage. ©2025 Old City Publishing, Inc.

关键词： Fourier transform infrared spectroscopy

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Optimizing Spot Instance Reliability and Security Using Cloud-Native Data and Tools

arXiv

引用

arXiv 2025年

作者： Saqib, Muhammad Malhotra, Shubham Mehta, Dipkumar Jangid, Jagdish Yashu, Fnu Dixit, Sachin Texas Tech University Dept. of Computer Science United States Rochester Institute of Technology Dept. of Software Engineering United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stony Brook University Dept. of Computer Science United States Stripe Inc United States

This paper presents "Cloudlab," a comprehensive, cloud-native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for "Security as Code," and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures. Copyright © 2025, The Authors. All rights reserved.

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Evaluating Fault Tolerance and Scalability in Distributed File Systems: A Case Study of GFS, HDFS, and MinIO

arXiv

引用

arXiv 2025年

作者： Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin Rochester Institute of Technology Dept. of Software Engineering United States Stony Brook University Dept. of Computer Science United States Texas Tech University Dept. of Computer Science United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stripe Inc United States

Distributed File Systems (DFS) are essential for managing vast datasets across multiple servers, offering benefits in scalability, fault tolerance, and data accessibility. This paper presents a comprehensive evaluation of three prominent DFSs—Google File System (GFS), Hadoop Distributed File System (HDFS), and MinIO—focusing on their fault tolerance mechanisms and scalability under varying data loads and client demands. Through detailed analysis, how these systems handle data redundancy, server failures, and client access protocols, ensuring reliability in dynamic, large-scale environments is assessed. In addition, the impact of system design on performance, particularly in distributed cloud and computing architectures is assessed. By comparing the strengths and limitations of each DFS, the paper provides practical insights for selecting the most appropriate system for different enterprise needs, from high-availability storage to big data analytics. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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A Big Data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

引用

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

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big Data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big Data Real-time systems Internet of Things Sustainable development Water resources

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

引用

Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

引用

29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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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...

引用

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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A novel energy-efficient cross-layer design for scheduling and routing in 6TiSCH networks

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computer Communications 2025年 239卷

作者： Hannachi, Ahlam Jaafar, Wael Bitam, Salim Ouazene, Nabil University of Biskra Dept. Computer Science BP 145 RP Biskra Biskra07000 Algeria Dept. Software and IT Engineering 1100 R. Notre Dame W. MontrealQCH3C 1K3 Canada 53. Constantine Road Batna Fesdis05078 Algeria

The 6TiSCH protocol stack plays a vital role in enabling reliable and energy-efficient communications for the Industrial Internet of Things (IIoT). However, it faces challenges, including prolonged network formation, inefficient parent switching, high control packet overhead, and suboptimal resource utilization. To tackle these issues, we propose in this paper a novel cross-layer optimization framework aiming to enhance the coordination between the Scheduling Function (SF), the Routing Protocol for Low-Power and Lossy Networks (RPL), and queue management. Our solution introduces a slot-aware parent switching mechanism, early slot reservation to mitigate queue overflow, and a refined slot locking strategy to improve slot availability. To reduce control overhead, the proposed method merges 6P cell reservation information into RPL control packets (DIO/DAO), thus minimizing control exchanges during parent switching and node joining. Optimized slot selection further reduces latency and jitter. Through extensive simulations on the 6TiSCH simulator and under varying network densities and traffic loads, we demonstrate significant improvements over the standard 6TiSCH benchmark in terms of traffic load, joining time, latency, and energy efficiency. These enhancements make the proposed solution suitable for time-sensitive IIoT applications. © 2025 Elsevier B.V.

关键词： Industrial internet of things (IIoT)

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