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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Predicting COPD Severity Using Machine Learning and GOLD Criteria 3

Predicting COPD Severity Using Machine Learning and GOLD Cri...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Kinikar, Atharva Chandwani, Maanav Rane, Tushar Pune Institute of Computer Technology Dept. Information Technology Pune India Pune Institute of Computer Technology Dept. Computer Engineering Pune India

ISBN: (纸本)9798350381931

Breathing problems that get worse over time are a symptom of chronic obstructive pulmonary disease, or COPD. It is a catch-all phrase for a number of conditions including asthma, bronchitis, and emphysema. It is defined by a progressive constriction of the airways and an early aging of the lungs. Experts have attested to the fact that early identification helps lessen the symptoms of COPD. Thus, using GOLD criteria, patients' COPD severity can be predicted using contemporary methods like machine learning algorithms. The proposed prediction models predict severity of COPD of the patient on a scale of 1 to 4 as prescribed by the GOLD system. Our model was trained to utilize a derived feature set and machine learning classifiers, such as SVC, Random Forest Classifier, XGBoost classifier, Naive Bayes, and Hard Voting Ensemble, with the best possible parameter choice to achieve a commendable accuracy of 97.6%. The global COPD epidemic will be lessened with the use of this method and medical professionals' experience. © 2024 IEEE.

关键词： Machine learning

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Deep Learning Assisted Intelligent Human computer Interaction for Next Generation Internet Applications

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 145-146页

作者： Hasan, Al Mehedi Hwang, Yong Seok Shin, Jungpil Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology Bangladesh Kwangwoon University Seoul Korea Republic of School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

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Leveraging Deep Quantum Convolutional Neural Networks for Student Facial Expression Identifcation and Mode Assessments 4

Leveraging Deep Quantum Convolutional Neural Networks for St...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Hossain, Javed Alam, Md. Nur Akib, Mohammad Yasin Nur Kogoya, Krisdahim Nanjing University of Information Science and Technology Dept. of Artificial Intelligence Nanjing China Nanjing University of Information Science and Technology Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331529635

The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. This study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58 © 2024 IEEE.

关键词： Convolutional neural networks

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Scheduling of Flexible Manufacturing System with Penalty Cost Using Simulated Genetic Algorithm 15

Scheduling of Flexible Manufacturing System with Penalty Cos...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Vijai Singh, Varsha Inderprastha Engineering College dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India Indian Institute of Information Technology Allahabad dept. of Information Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350370249

This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling is considered to be a computationally hard problem. In this paper, various approaches are reviewed that are applied to the FMS scheduling problem. This paper also attempts to find a possible solution to the scheduling problem of FMS. To identify the order in which the various jobs would be assigned to a number of machines, this paper uses a genetic algorithm with Simulated Annealing to generate a machine sequence. In this, the developed tool requires tabular input. To obtain a schedule with minimum make span, many iterations of the genetic algorithm are applied. In this system, an optimal schedule is given for FMS by taking the makespan time and minimum penalty cost, and therefore calculating the minimum combined objective function. © 2024 IEEE.

关键词： Scheduling algorithms

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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration 2

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Harshit Middha, Parth Thakur, Ayush Singh, Sofia Saraswat, Shipra Bhatia, Neha Amity University Dept. of Computer Science & Engineering Noida India Amity Institute of Information Technology Amity university Noida India Panipat Institute of Engineering & Technology Dept. of Information Technology Panipat India

ISBN: (纸本)9798331519056

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence. © 2024 IEEE.

关键词： Accelerated Training Hidden Weights Neural Networks Spectral Relaxation X-Iteration

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Free Space Optical Communication Using Principles of Quantum Mechanics 4

Free Space Optical Communication Using Principles of Quantum...

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4th International Conference on Innovations in computer science, ICONICS 2024

作者： Angabeen, Tooba Asghar, Fatima Ali Zafar, Roohi Malik, Tahir Khan, Muhammad Mubashir Kamran, Muhammad NED University of Engineering and Technology Dept. of Physics Karachi Pakistan NED University of Engineering and Technology Dept. of Telecommunications Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9798331504342

It all begun by the early 1970s through, the work of Stephen Wiesner and Gilles Brassard, who introduced the concept of quantum conjugate coding, and it has now stretched to a level where today's agile communication systems constitute a substantial challenge for data security and safety. A number of research has been performed to achieve secure communication among legitimate parties in which QKD (Quantum Key Distribution) is prominent. Quantum Key Distribution (QKD), also called Quantum Cryptography, enables two communicating parties to share secret cryptographic keys with security that is theoretically guaranteed by the laws of quantum mechanics, assuming ideal conditions. However, practical implementations may be vulnerable to certain attacks, and the security of QKD can be compromised by imperfections in hardware, side-channel attacks, or limitations in real-world setups. In a practical QKD setup, two stations send and receive encoded quantum particles and some classical information. The most rudimentary and well-known QKD protocol is BB84, developed by Bennet and Brassard in 1984. We have developed the simulation and achieved the secret shared key with and without an intruder. © 2024 IEEE.

关键词： Optical communication

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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