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5th International Conference on Advancements in Computing, ICAC 2023

作者： Sathintha, Tharuka Basnayake, Omal Wickramasinghe, Piyumi Dharmakeerthi, Rusiru Dharmakeerthi, Uditha Jayakody, Anuradha Sri Lanka Institute of Information Technology Department of Computer Systems Networking Malabe Sri Lanka

ISBN: (纸本)9798350358131

This proposal outlines the development of a mobile application aimed at enhancing the learning capabilities of Sri Lankan kindergarteners. Through the improvement of early writing and speaking skills, emotion recognition, and personalized memory assessments, the application seeks to identify and address learning gaps in young children. The application's core focus includes four key components: refining writing skills through guided practice, empowering parents to identify and track their child's weaknesses and progress, enabling accurate pronunciation practice and tracking, and promoting emotional awareness by identifying and reflecting emotions through music. Additionally, the app offers tailored memory assessments via a mobile game, catering to individual learning needs and fostering cognitive growth. Extensive research, collaboration with educators, psychologists, and parents, as well as usability testing, contribute to the app's effectiveness and user-friendliness. By providing personalized learning experiences, the mobile application has the potential to significantly enhance educational outcomes for Sri Lankan children, equipping them with essential skills in writing, speaking, emotional intelligence, and memory retention. © 2023 IEEE.

关键词： E-learning

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A Review Paper: Security for Supervisory Control and Data Acquisition SCADA Based on DNP3 16

A Review Paper: Security for Supervisory Control and Data Ac...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Jasim, Amenh A. Alheeti, Khattab M. Ali University of Anbar College of Computer and Information Technology Computer Science Department Ramadi Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Ramadi Iraq

ISBN: (纸本)9798350381344

SCADA (Supervisory Control and Data Acquisition) is a crucial ICS used in gas pipelines, power grids, and healthcare, this study discusses the importance of intrusion detection systems (IDS) systems, highlighting the DNP3 protocol for communication, IDS intrusion detection systems, and assaults. It compares and debates past research on the topic, emphasizing the need to detect irregularities and maintain safety in SCADA systems, as security breaches pose significant risks. © 2023 IEEE.

关键词： Intrusion detection

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Integrating Business Intelligence and Recommendation Marketplace System for Hawker Using Content Based Filtering 4

Integrating Business Intelligence and Recommendation Marketp...

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Mishan, Mohd Taufik Amir, Aimi Liyana Bin Mohd Supir, Mohd Hafifi Kushan, Albin Lemuel Zulkifli, Norlindamalia Rahmat, Mohammad Hafidz Department of Computer Sciences Melaka Malaysia Melaka International College of Science and Technology Faculty of Information Technology and Multimedia Melaka Malaysia Department of Computer Technology Networking Melaka Malaysia

ISBN: (纸本)9798350318432

The hawker industry is known for its diverse food options and affordable prices, but customers often struggle to select their preferred hawker stalls due to the abundance of choices. To address this challenge, this research proposes the integration of business intelligence techniques with a recommendation marketplace system using content-based filtering. This research aims to enhance the hawker dining experience by providing personalised recommendations based on customer preferences and leveraging data-driven insights. The objectives of this study are to collect and integrate data from various sources, including hawker stall information, customer preferences, and feedback. This research will employ content-based filtering techniques to analyse this data and develop a recommendation engine that suggests hawker stalls and food options aligned with users' preferences and past interactions. Through this integration, this project seeks to derive valuable insights from the collected data, such as identifying popular food categories, analysing customer behaviour, and understanding customer satisfaction levels. These insights will enable us to optimise the recommendation marketplace system, improve operational efficiency, and enhance the overall customer experience. This paper presents a mobile-based recommendation system that allows customers to explore personalised hawker stall recommendations based on their preferences. By integrating business intelligence with a recommendation marketplace system for hawker food, this research contributes to improving the decision-making process for customers, promoting hawker stall visibility and revenue, and creating a personalised and enjoyable dining experience. © 2023 IEEE.

关键词： Recommender systems

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A Real-Time Acceleration-Based Frailty Detection System for Elderly Care 17

A Real-Time Acceleration-Based Frailty Detection System for ...

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17th International Convention on Rehabilitation Engineering and Assistive technology, i-CREATe 2024

作者： Sansrimahachai, Watsawee Toachoodee, Manachai Khemapech, Ittipong School of Science and Technology University of the Thai Chamber of Commerce System and Networking Group Computer Science Department Bangkok Thailand University of the Thai Chamber of Commerce System and Networking Group School of Science and Technology Bangkok Thailand

ISBN: (纸本)9798350355154

Over the past decade, almost all countries in the world have experiencing growth in both the size and the proportion of elderly people in the population. The number of world's aging population is expected to reach about 2.1 billion by 2050, which is more than double figure from 1 billion in 2020. This statistic indicates the fact that we are entering the era of 'super-aged society'. One of the major problems in the elderly comes in the form of a decline in health and physical function, generally called Frailty. The elderly with frailty typically suffer several health issues including loss of muscle mass, poor gait and balance and cognitive impairment. They are higher risk of falls and quick progression to dependency. Early identification of frailty is required to effectively reduce and prevent frailty outcomes. To address the problem, we propose a frailty detection system that can remotely analyze gait characteristics and detect frailty among elderly individuals in real-time. Our system utilizes an Ultra-wideband tag attached to the body of the elderly as a data collection device. It applies data stream mining techniques that perform machine learning-based gait analysis and detect frailty based on the patterns of acceleration data. The experimental result indicates that our proposed frailty detection system is practical to be used in real-world settings with relatively high accuracy rate, about 81.8% for overall accuracy. © 2024 IEEE.

关键词： Frailty Detection Gait Analysis Stream Mining

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Metrics Evaluation of Bell Pepper Disease Classification Using Deep Convolutional Neural Network (DCNN) 1

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2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Sowmiya, K. Thenmozhi, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

Crop disease is one of the major issues in agricultural fields. It reduces the food production process and produces a huge economic loss for farmers in agricultural lands. Deep learning models are built for detecting and identifying diseases at an early stage to help the farmers and provide food security. In this paper, transfer learning approach is used to identify and classify the diseased portion of leaves in farming lands. The benchmark dataset was taken from the Internet (Kaggle) for bell pepper leaf for identifying the diseases such as pepper leaf spot, mosaic virus, blight, damping off disease, verticillium wilt, blossom end rot, yellowing leaves, curling leaves, and healthy leaves. Ten pre-trained models are built for recognizing diseases and compared to obtain better accuracy. Adam optimizer and binary cross-entropy is used in the model for calculating loss function. In this paper, experimental results are presented on bell pepper diseases for MobileNetV2 with better accuracy of 99.42%. The various performance metrics such as accuracy, precision, recall and F1-score, ROC curve are used to determine the accuracy of the model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Integration of IoT-enabled UAVs in Smart Agriculture: Types, Applications, Trends, challenges 7

Integration of IoT-enabled UAVs in Smart Agriculture: Types,...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prathyusha, Gajawada Krishnan, M.B.Mukesh Deepak, S.Prem Kumar SRM Institute of Science and Technology School of Computing Department of Networking and Communications India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for various applications, including the military, smart agriculture, rescue operations, entertainment, monitoring, and medicine delivery. In the current era, UAVs extensively employ a wide range of technologies to transmit data to base stations. The collected data undergoes processing, producing remarkable outcomes through the integration of machine learning and deep learning technologies. This paper reviews disease identification and object detection techniques and their associated applications & challenges. Additionally, the research explores recent trends and technologies in the field of smart agriculture. This research emphasizes the use of sensors in unmanned aerial vehicles (UAVs) by analyzing the methods utilized for recognizing diseased areas and things while identifying insects and sick crops. This paper also addresses the challenges encountered by existing methodologies and proposes directions for future research and development. Furthermore, this review delves into suitable techniques for heterogeneous UAVs and examines IoT-related strategies. © 2024 IEEE.

关键词： Smart agriculture

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Performance Evaluation Comparison Between Central SDN Network and DSDN 7th

Performance Evaluation Comparison Between Central SDN Networ...

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7th National Conference on New Trends in Information and Communications technology Applications, NTICT 2023

作者： Oleiwi, Wed Kadhim Abdullah, Alharith A. Department of Computer College of Science for Women University of Babylon Babylon Iraq Department of Networking College of Information Technology University of Babylon Babylon Iraq

ISBN: (纸本)9783031628139

Software-Defined networking (SDN) is a cutting-edge technology for increasing network programmability. Because of its centralized nature, SDN is particularly susceptible to single point of failure (SPoF) and scalability issues. Distributed SDN (DSDN) offers to eliminate the single point of failure;security issue, and scalability that present in the centralize SDN controller. The main concept is to have numerous controllers that can share the burden on the network, and one controller can take over another controller when it *** this paper we work on Performance Evaluation comparison between central SDN network and DSDN, The Result Of Performance Evaluation Comparison Between Central SDN network and DSDN show that the dropped packet of (SPoF) in central SDN was 100% while the percentage of Dropped Packet decrease to 12%, and 22% as one controller, two controllers respectively fail in DSDN. The performance evaluation of Topology Discover shows there are a Progressive relationship between the time of Topology Discover and number of nodes in data plane. The Throughput of distributed Opendaylight controllers is 16.8% points higher than that of the centralized controller, while the centralized Opendaylight controller has a 22.4% greater Latency than distributed SDN controllers;where each of Throughput and Latency are SDN network Scalability metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Controllers

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Mitigating Credit Card Fraud: A Machine Learning Perspective 2

Mitigating Credit Card Fraud: A Machine Learning Perspective

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Bhushan, Shivam Gupta, Indrajeet Angayarkanni, S.A. Balakiruthiga, B. Pandimurugan, V. Rajaram, V. School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India Acharya Institute of Technology Computer Science and engineering Bangalore India

ISBN: (纸本)9798350366570

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape © 2024 IEEE.

关键词： Random forests

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A Robust Emergency Messages Routing Scheme for Urban VANETs

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computers, Materials & Continua 2022年第8期72卷 2617-2632页

作者： Mumtaz Ali Shah Farrukh Zeeshan Khan Ghulam Abbas Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan

Vehicular ad-hoc networks(VANETs)play an essential role in enhancing transport infrastructure by making vehicles intelligent and proficient in preventing traffic ***-based greedy protocols pick the next route vehicle for transmitting emergency messages(EMs)depending upon the present location of adjacent vehicles towards sink vehicles by using an optimal uni-directional road traffic ***,such protocols suffer performance degradation by ignoring the moving directions of vehicles in bi-directional road traffic where topological changes happen *** to the high number of vehicles,it is essential to broadcast EMs to all vehicles to prevent traffic delays and collisions.A cluster-based EM transmitting technique is proposed in this *** urban VANETs,this paper pioneers the clustering of bi-directional road traffic for robust and efficient routing of *** this regard,this paper introduces a routing protocol,namely,the bi-directional urban routing protocol(BURP).In addition to the paths and relative locations of vehicles,BURP takes account of the distance parameter by using the Hamming distance function to determine the direction ofmotion of vehicles and communicates EMs through the cluster head(CH).Amodified k-medoids algorithm is presented for the clustering of bi-directional road traffic.A median method is presented for selecting CH to ensure the longrunning of a *** results show that BURP provides enhanced throughput,a maximized packet delivery ratio,low energy consumption,and network delay relative to eminent routing protocols.

关键词： Vehicular ad hoc networks bi-directional road traffic routing protocol emergency message broadcasting direction-based greedy forwarding

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