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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, Machine Learning and Artificial Intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Study on flood probability by neural network prediction based on the importance of random forest characteristics 3

Study on flood probability by neural network prediction base...

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3rd International Conference on Electrical, Electronics, and information engineering, EEIE 2024

作者： Zhang, Yujun Kang, Yu Liu, Yupeng Fan, Rulong Duan, Jianfu School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information and Communication Engineering Beijing University of Posts and Telecommunications China School of Economics and Management Taiyuan University of Technology China

ISBN: (纸本)9781510688179

In recent years, global climate change and human activities have intensified the frequency and severity of floods. Based on the big data of flood probability, combined with the index data of monsoon intensity, river management, deforestation, urbanization and climate change, this paper studies from the perspective of flood probability prediction. Firstly, this paper selects indicators and collects corresponding data, and processes the data. Then, the importance of index characteristics is evaluated by the random forest algorithm. Secondly, the number of final indicators is determined by kernel principal component analysis, and the final indicators are determined by combining the importance of features. Then, the number of classifications is determined by the AIC/BIC criterion, and the risk is classified into three categories: high, medium, and low, by GMM clustering, based on the EM algorithm. Finally, 60% of the data is used as the training set, 30% of the data as the verification set, and 10% of the data as the test set to divide the data set, and then it is brought into the neural network prediction model for prediction. Finally, by analyzing the clustering results and prediction results, it provides a scientific basis and a practical application scheme for disaster prevention and reduction. © The Authors.

关键词： Flood damage

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Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

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computer Systems Science & engineering 2022年第3期40卷 1199-1215页

作者： Vasaki Ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion ***-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired *** survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis *** paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific ***,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement *** main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless *** detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural *** this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless *** main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these *** it is very crucial to design an IDS specifically to target on the wireless networks.

关键词： Internet of Things MANET intrusion detection systems wireless networks

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A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture 9th

A Self-Attention-Based Stock Prediction Method Using Long Sh...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Ye, Xiaojun Ning, Beixi Bian, Pengyuan Feng, Xiaoning College of Computer Science and Technology Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9789819959679

The ability to analyze the trend of the stock market has always been paid high attention to. A large number of machine learning technologies have been used for stock analysis and prediction. The traditional time series prediction models, including RNN, LSTM and their deformed bodies, show the problems of gradient disappearance and low efficiency in long-span prediction. This paper proposes a long-term and short-term memory network architecture, which based on Encoder and Decoder Stacks and self-attention mechanism, replacing the feature extraction part of traditional LSTM through self-attention mechanism and provides interpretable insights into the dynamics of time. Through the results of simulation experiments, this paper shows the comparison of stock prediction effects through using RNN, Bi-LSTM and Encoder and Decoder-Attention-LSTM models. The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model, and can achieve high accuracy when the epoch is small. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Joint Radio Frequency and Optical Spectral Optimization for High-Speed Wide Area Fiber Optic Communications 3

Joint Radio Frequency and Optical Spectral Optimization for ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vivek, V. Kumar, Aditya Yuvaraj, S. Vaishali, A. Katyal, Ashu Sahoo, Girija Shankar Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper affords a progressive approach to high-pace wide area fiber optic (WAFO) communications by way of combining radio frequency (RF) and optical spectral optimization strategies. This method presents a new approach to the longstanding task of reaching high-pace, long-distance transmission over current networks. The optimization consists of maximizing the spectral performance of the RF and optical structures with the aid of the use of techniques which includes frequency-agile modulation codec's, linear dispersion compensation, and spectral shaping strategies. Moreover, dynamic optical and RF network routing algorithms are proposed to enhance potential and reduce latency. The proposed optimization is confirmed thru enormous simulation and numerical analysis. Results indicate big overall performance gains in phrases of spectral performance, records price, postpone, and reliability. This joint RF-optical optimization approach is an efficient and value-effective answer for WAFO networks and ought to notably benefit large-scale and future WAFO deployments. © 2023 IEEE.

关键词： Compensation Deployments Enormous Performance Reduce

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Toward Utilizing Bidirectional Multi-head Attention Technique for Automatic Correction of Grammatical Errors

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1886-1897页

作者： Mahmoud, Zeinab Kryvinska, Natalia Abdalsalm, Mohammed Solyman, Aiman Alfatemi, Ali Musyafa, Ahmad School of Computer Science and Technology Wuhan University of Technology China Department of Information Management and Business Systems Comenius University Slovakia Department of Computer Science University of Milan Italy CIS Department Fordham University BronxNY10458 United States Department of Informatics Engineering Pamulang University South Tangerang15310 Indonesia

Automatic Grammar Error Correction (GEC) models identify and correct a wide range of grammatical errors. Various strategies have been proposed for GEC, with the Neural Machine Translation (NMT) approach being the most effective. However, NMT-based GEC models with encoder-decoder layers rely heavily on the highest layer, leading to potential inaccuracies. Additionally, during inference, exposure bias can cause the model to substitute previously targeted words with incorrect alternatives. Another challenge is data scarcity. This paper introduces a GEC model leveraging the seq-to-seq Transformer framework, specifically designed for low-resource languages like Arabic. We propose a method to generate noise in the text to create synthetic parallel data, addressing the data constraints. Inspired by Capsule Networks (CapsNet), we incorporate CapsNet in GEC to dynamically aggregate information from multiple layers. In order to mitigate exposure bias, we incorporated a bidirectional training approach and a regularization term using Kullback-Leibler divergence to align left-to-right and right-to-left models. Experiments on two benchmarks demonstrate that our model outperforms current Arabic GEC models, achieving the highest scores. The code is available on GitHub (https://***/Zainabobied/ArabicGEC). © (2024), (International Association of Engineers). All rights reserved.

关键词： Neural machine translation

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Saline Infusion Monitoring and Control System Using IoT 2

Saline Infusion Monitoring and Control System Using IoT

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Ranjani, J. Hariharan, Shanmugasundaram Jeyashree, Anupama Aishwarya, E. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350347456

The use of technology in healthcare is becoming increasingly common with the growth of population and advancements in technology. The healthcare industry is integrating engineering principles to create automated systems instead of manual labor. So, we have proposed an IoT-based saline infusion monitoring system which can greatly improve patient safety by continuously monitoring the saline level using a level sensor and alerting healthcare providers if it falls below 10%. The system includes sensors to monitor the patient's blood pressure and heart rate, and a connection module for remote monitoring to send the alert notifications. The system stops the flow of saline if the level is low, ensuring that the patient's vein is not damaged. This system can significantly improve patient care and minimize the need for monitoring and management. © 2023 IEEE.

关键词： Blood pressure

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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Resource Management with Blockchain for Delay-Sensitive Transmission in UAV-assisted Vehicular Network 4

Resource Management with Blockchain for Delay-Sensitive Tran...

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4th International Conference on Neural Networks, information and Communication engineering, NNICE 2024

作者： Chen, Xuqiang Zhang, Guobin Han, Guojun Peng, Zexin Dongguan University of Technology School of Computer Science and Technology Dongguan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China Guangdong University of Technology School of Information Engineering Guangzhou China Shenzhen Urban Transport Planning Center Model Institute Shenzhen China

ISBN: (纸本)9798350394375

In this paper, we present a blockchain based resource allocation scheme on UAV-assisted communication in vehicular network. Our design aims to minimize the system delay by coordinating the spectrum sharing and power consumption of the secondary UAVs under the tolerated interference limitation of the primary UAVs. In the main process of the blockchain application, firstly, the spectrum request of the UAVs is formulated as a multi-agent reinforcement learning problem to select the channel and power value. Then, within a limited power range and time delay requirements, the resource request is broadcasted, and the voting is conducted according to the Ripple consensus mechanism. Finally, the transaction record is packaged into a new ledger and attached to the whole ledgers. Simulation results show that the proposed blockchain-based resource management scheme can improve the communication reliability, real-time character and vehicular rate. © 2024 IEEE.

关键词： Blockchain

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AI-Based Portable Gesture Recognition System for Hearing Impaired People Using Wearable Sensors 15

AI-Based Portable Gesture Recognition System for Hearing Imp...

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15th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Al-Qaisy, Noor E. Al-Kaseem, Bilal R. Al-Dunainawi, Yousif College of Engineering Al-Iraqia University Computer Engineering Department Baghdad Iraq College of Engineering and Information Technology AlShaab University Baghdad Iraq

ISBN: (纸本)9798350335149

Recently, there has been a remarkable interest in sign language recognition techniques. Especially in the field of sensor-based besides the extensive employment of open-source platforms in research and development testbeds. Sign language recognition has attracted considerable attention from academic scholars and the industry because deafness recognized as a severe and worldwide health concern. However, most studies in recognition have only focused on vision-based or image-based systems that were not suitable for outdoor usage and lack mobility features. This paper introduces a smart glove that is based on wearable sensors to achieve portable standalone system working in a real-time environment with a user-friendly interface. The presented system utilized modern approaches to collect and generate new datasets using two kinds of sensors only. This dataset was employed to develop an artificial neural network (ANN) model that was capable of predicting the alphabetic letters based on hand gestures and orientation. The ANN model was trained using Scaled Conjugate Gradient (SCG) algorithm. The obtained results showed a remarkable performance in terms of ANN accuracy for both Arabic Sign Language (ArSL) and American Sign Language (ASL) which were 96%, 98% respectively. The performance of the developed ANN model ensured its usability in real-time scenario. © 2023 IEEE.

关键词： Wearable sensors

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