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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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An Anomaly Detection System Based on Clustering and Fuzzy Set Theory in VANETs 8th

An Anomaly Detection System Based on Clustering and Fuzzy Se...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Kuchaki Rafsanjani, Marjan Fatemidokht, Hamideh Balas, Valentina Emilia Batth, Ranbir Singh Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Young Research Society Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9783030521899

In vehicular ad hoc network (VANET), which is considered as a type of mobile ad hoc network (MANET), communication occurs between neighbor vehicles and also roadside equipment and vehicles. Due to the characteristics of VANET, there are chances of a number possible attack in this network. In this paper, an anomaly detection system that uses clustering and fuzzy set theory to defend the Denial of Service (DoS) attack is presented. Results of simulation represent that the proposed algorithm arrives to the high detection rate. © 2021, Springer Nature Switzerland AG.

关键词： Fuzzy set theory

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Aqib Hassan Vengas Memon Farhan Bashir Shaikh Saad Alahmari Mansoor Iqbal Farooq Azam School of computing and mathematical sciences University of Greenwich London United Kingdom Faculty of Information and Communication Tech. Universiti Tunku Abdul Rahman Perak Malaysia Department of Computer Science Northern Border University Arar Saudi Arabia Department of Computer Science University of Wah Wah Cantt Pakistan Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability.

关键词： TCP Transport protocols Protocols Force Packet loss Throughput Real-time systems Stability analysis Reliability Low latency communication

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Digital Image Recovery Based on Lifting Wavelet Transform 1

Digital Image Recovery Based on Lifting Wavelet Transform

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1st International Conference on engineering and Technology, ICoEngTech 2021

作者： Taha, Taha Basheer Taha, Dujan B. Ngadiran, Ruzelita Ehkhan, Phaklen IT Department Faculty of Sciences TISHK International University Erbil Iraq Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Random forest‑based prediction of stroke outcome

arXiv

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arXiv 2024年

作者： Fernandez‑Lozano, Carlos Hervella, Pablo Mato‑Abad, Virginia Rodríguez‑Yáñez, Manuel Suárez‑Garaboa, Sonia López‑Dequidt, Iria Estany‑Gestal, Ana Sobrino, Tomás Campos, Francisco Castillo, José Rodríguez‑Yáñez, Santiago Iglesias‑Rey, Ramón Department of Computer Science and Information Technologies Faculty of Computer Science CITIC-Research Center of Information and Communication Technologies Universidade da Coruña A Coruña Spain SERGAS Universidade da Coruña A Coruña Spain Santiago de Compostela Spain Software Engineering Laboratory Department of Computer Science and Information Technologies Faculty of Computer Science University of A Coruña Campus de Elviña A Coruña15071 Spain Hospital Clínico Universitario Rúa Travesa da Choupana s/n Santiago de Compostela15706 Spain Santiago de Compostela Spain

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3‑months after admission. The dataset consisted of patients with ischemic stroke (IS) and non‑traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/ morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3‑months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p‑value © 2024, CC BY.

关键词： Forecasting

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Deep Learning Approach for COVID-19 Identification 18

Deep Learning Approach for COVID-19 Identification

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18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Ul Haq, Amin Li, Jian Ping Khan, Riaz Ullah Mawuli, Cobbinah Bernard Agbley, Bless Lord Y. Raj, Mordecai F. Zhou, Wang Khan, Jalaluddin Haq, Abdul Saboor, Abdus Habib, Faiza Khan, Zafar University of Electronic Science and Tech. of China School of Computer Science and Engineering Chengdu China 313000 China Xihua University School of Computer and Software Engineering Chengdu Sichuan China University of Electronic Science and Tech. of China School of Computer Science and Engineering Chengdu China Gomal University Department of Computer Science D.I. Khan29220 Pakistan Hazzra University Department of Computer Science and IT Manshera KPK 21120 Pakistan Northern University Faculty of Engineering and I.T Nowshera Pakistan

ISBN: (纸本)9781665413640

Accurate diagnostic system is significantly important for timely COVID-19 identification. Diagnosing COVID-19 from chest x-ray images employing the CNN model is recommended for accurate recognition of COVID-19. The existing diagnosis techniques of COVID-19 still lack high accuracy. To handle this problem in this work, we have proposed accurate detection method for COVID-19. In the proposed method, a CNN is incorporated for the diagnosis of COVID-19 using chest x-ray images data. The experimental results illustrate that our technique is good for COVID-19 accurate diagnosis and can be easily implemented in health care systems. © 2021 IEEE.

关键词： COVID-19

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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Advancements in Computing (ICAC), International Conference on

作者： Naveen Warnakulasuriya Shalinika De Silva Janani Madushika Sadali Gamage Sanath Jayawardena Anuradha Karunasena Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce “NeuraTrace PD,” an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition.

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Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Selamu Shamore Amin Tuni Gure Mohammed Abebe Yimer Department of Computer Science Wachemo University Hossana Ethiopia Department of Information System Arsi University Arsi Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language.

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A Small-Signal Stability Criterion for a Power System Dynamic Model with Constant Power Loads

SSRN

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SSRN 2024年

作者： Levron, Yoash Belikov, Juri Weiss, George The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Tallinn12618 Estonia School of Electrical Engineering Tel-Aviv University Tel-Aviv69978 Israel

This theoretical paper continues a long line of research works about the stability of power systems represented by reduced order models. We provide a small-signal stability criterion for a dynamic model that uses time-varying phasors, and is characterized by a lossless transmission network and constant power loads located anywhere in the network. A key concept in this study is the matrix H of the partial derivatives of the generator powers with respect to their relative angles. Our main result is that the local stability of any equilibrium point is completely determined by properties of this matrix H. More specifically, we show that an equilibrium point is asymptotically stable if H is positive definite, and is unstable if H is full-rank and not positive definite. In addition, we also prove several relations between the eigenvalues of H and the eigenvalues of the system's Jacobian matrix. These results are demonstrated on a number of case-studies, and compared with related earlier ones from the literature. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Enhanced Security Protocol for VLSI Systems: Modified AES Algorithm for Robust Data Transmission 3

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lawrence, Jinsha Budagam, Dinesh Kumar Mukilan, P. Veerappan, Kavitha Chandrasekhar, E. Barathi, K. Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Visa Department of Ms in Software Engineering CA United States Dhanalakshmi Srinivasan College of Engineering Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India Department of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508456

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance. © 2025 IEEE.

关键词： Network security

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