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International Conference on Industrial and Information Systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts

An Integrated Approach to Enhance Legal Information Retrieva...

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Advancements in Computing (ICAC), International Conference on

作者： Asel Jayasooriya Akeel Ahamed Yomal Bandara Chamod Gavindya Dharshana Kasthurirathna Lakmini Abeywardhana Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept.of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice.

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics

Towards the Utilization of Parallel Programming to Speed Up ...

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International Conference on software Defined Systems (SDS)

作者： Ziad A. Al-Sharif Rahaf Alhamouri Yaser Jararweh Dept. of Engineering Computing and Mathematical Sciences Lewis University Romeoville USA Dept. of Software Engineering Jordan University of Sci. and Tech. Irbid Jordan Dept. of Computer Science Jordan University of Sci. and Tech. Irbid Jordan

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides $3X$ to $5X$ speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers.

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning

Identifying COVID-19 through X-ray and CT scan images using ...

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International Conference on Emerging Technologies, ICET

作者： Maham Shehzadi Usama Arshad Zainul Abideen Raja Hashim Ali Ammad Aslam Khan Ali Zeeshan Ijaz Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences Virtual University of Pakistan Rawalpindi Pakistan Dept. of Computer Science National University of Computer & Emerging Sciences Islamabad Pakistan

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management.

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Driver Drowsiness Detection with Region-of-Interest Selection Based Spatio-Temporal Deep Convolutional-LSTM

Driver Drowsiness Detection with Region-of-Interest Selectio...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Muhammad Saif Basit Usman Ahmad Jameel Ahmad Khalid Ijaz Syed Farooq Ali Dept. of Computer Science SST UMT Lahore Pakistan Dept. of Electrical Engineering SEN UMT Lahore Pakistan Dept. of Software Engineering SST UMT Lahore Pakistan

ISBN: (纸本)9781665464789

Driver fatigue and drowsiness instigate road traffic accidents while driving throughout the years. to reduce road traffic injuries and fatality cases, a real-time drowsiness detection system is needed by using artificial intelligence algorithms to detect drivers' tiredness and drowsiness at an early stage. This study proposes an automatic region-of-interest selection based stacked spatio-temporal convolution-long short-term memory (ConvLSTM) drowsiness detection neural network for an in-vehicle surveillance and security system. Haar Cascade classifiers are used to select the region-of-interest on the human face. A ConvLSTM model is implemented to extract spatio-temporal features from the selected region-of-interest and to predict the drowsiness state of the driver. The performance of the proposed model is compared with various pre-trained deep learning models such as CNN, VGG-16, VGG-19, ResNet-50 and MobileNet. The proposed model is trained on the Yawn Eye and MRL benchmarked image datasets. The proposed approach achieves an accuracy of 99.44% on the Yawn Eye dataset and 90.12% on the MRL dataset. The model is further tested and validated using a live feed camera.

关键词： Roads Surveillance Wheels Predictive models Feature extraction Cameras Safety

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Hybrid Deep Learning Approach for Nonfunctional software Requirements Classifications

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Kiramat Rahman Anwar Ghani Rashid Ahmad Syed Haseeb Sajjad Dept. of Software Engineering International Islamic University Islamabad Pakistan Big Data Research Center Jeju National University Jeju South Korea Dept. of Computer Science Inernational Islamic University Islamabad Pakistan

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts.

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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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LogVm: Variable Semantics Miner for Log Messages

LogVm: Variable Semantics Miner for Log Messages

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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Yintong Huo Yuxin Su Michael Lyu Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665476799

ISBN: (纸本)9781665476805

Modern automated log analytics rely on log events without paying attention to variables. However, variables, such as the return code (e.g., “404”) in logs, are noteworthy for their specific semantics of system running status. To unlock the critical bottleneck of mining such semantics from log messages, this study proposes LogVM with three components: (1) an encoder to capture the context information; (2) a pair matcher to resolve variable semantics; and (3) a word scorer to disambiguate different semantic roles. The experiments over seven widely-used software systems demonstrate that Log Vm can derive rich semantics from log messages. We believe such uncovered variable semantics can facilitate downstream applications for system maintainers.

关键词： Codes Conferences Semantics software systems software reliability Data mining

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