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22nd IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2024

作者： Ghosh, Partha Charit, Arnab Banerjee, Hindol Bandhu, Debanwesa Ghosh, Agniv Pal, Ankita Goto, Takaaki Sen, Soumya Academy of Technology Department of Computer Science and Engineering Adisaptagram India Toyo University Faculty of Information Sciences and Arts Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9798350391343

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets. © 2024 IEEE.

关键词： Students

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Classification of Malignant or Benign Cancer using Neural Networks 7

Classification of Malignant or Benign Cancer using Neural Ne...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Haidar, Gaby Abou Achkar, Roger Semaan, David Owayjan, Michel American University of Science and Technology Beirut Lebanon Faculty of Engineering and Computer Science Department of Computer and Communications Engineering Lebanon

ISBN: (纸本)9798350325850

Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification. © 2023 IEEE.

关键词： Diseases

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Endogenous Security of FQn Networks: Adaptive system-Level Fault Self-Diagnosis

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IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Influence of AI and MAS in Enhancing Energy Efficiency of 6G...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Negi, Divya Prakash, Febin Gupta, Anjali Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical And Electronics Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption. © 2024 IEEE.

关键词： Multi agent systems

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR 9

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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9th International Conference on Applying New technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

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React Agent-Based Question Answering Method for Biology in National College Entrance Examination 24

React Agent-Based Question Answering Method for Biology in N...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon's Communications

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IEEE Access 2023年 11卷 139345-139365页

作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Da Nang50000 Viet Nam

In a platoon communication scenario, the Platoon Leader (PL) vehicle interacts with the gNodeB (gNB) to obtain some assigned radio resources and then the obtained radio resources are allocated to the PL vehicle and its Platoon Member (PM) vehicles. The high transmission delay in the platoon network affects the quality of vehicles' connectivity. Therefore, the issues of improving radio resource's spectral efficiency and stability of the vehicle platooning with limited radio resources need to be tackled. This work proposes the 2-STage Resource Allocation (2-STRA) method for multi-platoon communication to reduce the transmission latency of platoon vehicles, i.e., it can improve the data transmission rates of platoon vehicles for the conditions of various speeds and various numbers of subchannels. In the $1^{st}$ stage, a greedy algorithm that jointly considers subchannel allocation and power control for PL vehicles' resource allocation is devised. In the $2^{nd}$ stage, the resource allocation for PM vehicles, which is based on the subchannels that are not allocated to PL vehicles, is resolved. A tripartite hypergraph, in which a tripartite hyperedge links:I 1) cluster of PM vehicles that use the same subchannel;2) individual cellular user;and 3) subchannel, is adopted to devise the PM vehicles' resource allocation algorithm. The simulation results show that the proposed 2-STRA method can have better performance in terms of spectral efficiency and the sum of data transmission rate comparing with the other methods. © 2013 IEEE.

关键词： Quality of service

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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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