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Advanced Research in computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models information systems

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An Analysis of Objective Function Modification Approaches in Routing Protocols for Low Power and Lossy Networks: A Fuzzy Logic-Based Perspective 9th

An Analysis of Objective Function Modification Approaches in...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Al-Qaisi, Laila Hassan, Suhaidi Zakaria, Nur Haryani Computer Network and Information Systems Department The World Islamic Sciences Education University Amman Jordan InterNetWorks Research Lab School of Computing Universiti Utara Malaysia Kedah Malaysia Data Management and Software Solution Research Lab School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9789819995882

The routing protocol known as RPL is employed in low power and lossy networks. It makes use of an objective function (OF) to establish a Destination Oriented Directed Acyclic Graph (DODAG) and ascertain the most suitable parental candidate or trip route. Nevertheless, the task of identifying a suitable OF in Low Power and Lossy Networks (LLN) is a significant challenge. The RPL was intentionally designed to possess a high degree of flexibility, allowing for the construction of routing topologies without imposing any specific routing metric or constraint. This design choice was made to accommodate the diverse range of LLN that exist. This study provides a critical overview of recent literature pertaining to the topic of RPL and specifically focuses on the many strategies aimed at enhancing OF inside the RPL protocol. The objective of this study is to provide an analysis of relevant endeavors, including the development of innovative metrics and the application of fuzzy logic techniques in the combination of OF metrics. Furthermore, this paper discusses the recommended augmentation strategies, as well as constraints and future development directions. The research community can employ the findings to gain a deeper comprehension of objective functions and improve the performance of RPL in the face of security problems. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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Impact of Artificial Intelligence on Decision-Making in information systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

关键词： Risk perception

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Autonomous sortie scheduling for carrier aircraft fleet under towing mode

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Defence technology(防务技术) 2025年第1期43卷 1-12页

作者： Zhilong Deng Xuanbo Liu Yuqi Dou Xichao Su Haixu Li Lei Wang Xinwei Wang Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China School of Mathematical Sciences Dalian University of TechnologyDalian 116024China Naval Aviation University Yantai 264001China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094China Department of Automation School of Information Science and TechnologyTsinghua UniversityBeijing 100084China

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft *** primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch *** this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource ***,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as *** analyzing the constraints in sortie scheduling,a mixed-integer planning model is *** particular,the constraint on preparing spot occupancy is improved to further enhance the sortie *** basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance *** efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is *** solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing *** animation of the simulation results is available at ***/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

关键词： Carrier aircraft Autonomous sortie scheduling Resource allocation Collision-avoidance Hybrid flow-shop scheduling problem

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Abnormal Image Classification Using Vision Transformer for Smart Agriculture 8

Abnormal Image Classification Using Vision Transformer for S...

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8th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hafeez, Talha Shahid, Muhammad Farrukh Khanzada, Tariq J.S. Fast School of Computing FAST-NUCES Karachi Pakistan King Abdulaziz University Faculty of Computing and Information Technology Department of Information System Jeddah21589 Saudi Arabia Mehran Uet Computer Systems Engineering Departments Jamshoro Pakistan

ISBN: (纸本)9798350327090

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset. © 2023 IEEE.

关键词： Classification (of information)

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Transforming the Academic System into a Trimester System in Higher Education: Case Study at KAU

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Journal of Operations and Strategic Planning 2023年第2期5卷 173 - 186页

作者： Shada Almarghalani Arwa Alqahtani Shahad Alzahrani Haya Alqahtani Salma Elhag Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The government of the Kingdom of Saudi Arabia is keen on the continuous development of higher education and the organization of continuous initiatives to improve the efficiency of the educational process and increase the quality of educational outputs. For this reason, it was adopted to transform the academic system in Saudi universities from the semester system to the trimester system as part of the transformation plan for the education system in the country. This study was applied to the Faculty of computing and information technology at King Abdulaziz University (KAU), to study the effect of using a trimester system in achieving the goals of the faculty’s strategic plan and contribute towards improving the quality of educational outcomes. A survey was implemented targeting the faculty students, to know their impressions and expectations about the system. The results summarize suggestions for implementing the system flexibly, contributing to facilitating students’ adaptation to this system and achieving its positive effects on their academic interests.

关键词： Higher education trimester system strategy plan strategic planning KAU

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Cloud Intrusion Detection System Based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies

Cloud Intrusion Detection System Based on Honeynet, Honeywel...

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International Conference on Data Science and Network Engineering, ICDSNE 2023

作者： Yasotha, B. Arthy, M. Shoba, L.K. Loganathan, Muralidaran Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Computer Science and Information Systems Engineering St. Joseph University Dares Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

The security aspect of cloud computing is much more challenging for researchers. Preventing the attack requires knowledge about the type of attack, its origin, and how vulnerabilities and tools are used for the attack. The cloud security methodology protects customer data, information, and applications from attackers. Due to digitalization, the volume of data is increased. The protection of data is very challenging for cloud service providers. This work proposes a new cloud intrusion detection system security infrastructure based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies. The proposed strategy effectively identifies intrusion detection, attack behavior, and attack scenario. The testing of data was carried out in OpenStack environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness Detection systems

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Informatica (Slovenia) 2024年第3期48卷 359-378页

作者： Latreche, Imene Slatnia, Sihem Kazar, Okba Barka, Ezedin Harous, Saad Department of Computer Science University of Mohamed Khider Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates Department of Information Systems and Security UAE University Al Ain United Arab Emirates Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Driver Drowsiness is considered one of the significant causes of road accidents and fatal injuries. Due to this, creating systems that can monitor drivers and detect early drowsiness has become an important field of research and a challenging task in recent years. Several research attempts were proposed to solve this problem based on several approaches and techniques. The Electroencephalogram (EEG) is one of the most efficient and reliable method, among the physiological signals-based monitoring approaches. In this area, many Machine Learning (ML) techniques have been used to detect EEG-based driver drowsiness. However, due to the limitations of ML techniques, many researchers have shifted their focus to the use of deep learning (DL) techniques, which have demonstrated superior performance in many fields including the physiological signals classification tasks. This paper reviews and discusses numerous new research papers that have proposed and implemented driver drowsiness detection systems based on EEG and deep learning techniques. In addition, we have outlined the limitations and difficulties of the existing works and highlighted and proposed some propositions that will help future field researchers enhance and generalize the results. Based on our thorough analysis, we have determined that the latest advancements in detecting driver drowsiness have employed the convolutional neural network (CNN) technique, which has demonstrated effective performance in classifying signals. Furthermore, the primary issue encountered in all works is developing a more precise and accurate method. Nevertheless, we seek a precise system capable of swiftly identifying a state of drowsiness while using minimal spatial memory and processing resources. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Electroencephalography

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Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices

arXiv

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arXiv 2025年

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Abdul Majeed, Anwar P.P. Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Taicang Suzhou215400 China

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system's low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. Copyright © 2025, The Authors. All rights reserved.

关键词： Air quality

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