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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Xie, Lindong Li, Genghui Lin, Kangnian Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350393521

The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems. © 2023 IEEE.

关键词： Evolutionary algorithms

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Area and Energy Efficient Method Using AI for Noise Cancellation in Ear Phones

Area and Energy Efficient Method Using AI for Noise Cancella...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Manikandan, R. Ramkumar, S. Prabhakaran, M. Department of Lean Operations and Systems Karnataka Bengaluru India Department of Computer Science Karnataka Bengaluru India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350300857

Adaptive filters are suitable for most of the Digital Signal Processing (DSP) applications such as channel equalization, noise cancellation, echo cancellation, channel estimation and system identification. Nowadays due to the advancement in semiconductor technology, the need for Active Noise Cancellation (ANC) headphones in compact devices is increased. The major idea behind this proposed work is to design an area and energy efficient novel adaptive filter suitable for in-ear headphones by combining Normalized Least Mean Square (NLMS) and Block LMS (BLMS). The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed design mitigates the unwanted noises in various frequency bands. © 2023 IEEE.

关键词： Adaptive filters

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Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： R. Rajagopal Chaithanya. k Ankita Sonth Prasanna Basanagouda Kallappagoudar Mohammadzaid Mohammadishaq Nadaf Ananda Theertha H Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Dept of Computer Science Alliance College of Engineering and Design Bangalore India Dept of Computer Science and Engineering Alliance College of Engineering and Design Bangalore India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used.

关键词： Food waste Measurement Analytical models Machine learning algorithms Accuracy Computational modeling Nearest neighbor methods Data models Convolutional neural networks Random forests

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Lattice-Based Cryptography and NTRU: Quantum-Resistant Encryption Algorithms 4

Lattice-Based Cryptography and NTRU: Quantum-Resistant Encry...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Nisha, F. Lenin, J. Saravanan, S.K. Rohit, V. Robin Selvam, P.D. Rajmohan, M. Cmr University School of Engineering and Technology Computer Science & Engineering Karnataka Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science & Engineering Karnataka Bengaluru India Simats Saveetha School of Engineering Department of BigData and Network Security Tamil Nadu Chennai India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Karnataka Bengaluru India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349856

The quantum-resistant qualities of various encryption methods, such as N-th degree Truncated polynomial Ring Units (NTRU) Encrypt, NTRU Sign, Ring-Lizard, and Kyber protocols are becoming more important in light of the ever-evolving nature of digital threats. An-depth analysis of the mathematical derivation of these algorithms, as well as a determination of their computing efficiency and resistance to quantum assaults is discussed in this paper. Techniques of the Ring-Lizard and Kyber algorithms to lattice-based encryption are analyzed. The study gives an overview of quantum-resistant capabilities of these algorithms and their performance measures. Strengths and weaknesses of the NTRU Encrypt, NTRU Sign, Ring-Lizard, and Kyber algorithms are discussed. The results give useful insights for cybersecurity practitioners, academics, and policymakers, aiding them in making educated choices to protect digital infrastructures against quantum attacks. © 2024 IEEE.

关键词： Polynomials

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Innovations and Challenges in Green Cloud Computing via LEED, Ant Colony Optimization 3

Innovations and Challenges in Green Cloud Computing via LEED...

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

作者： Rohit, V. Robin Saravanan, S.K. Lenin, J. Nisha, F. Sivakumar, S. Rajmohan, M. Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Saveetha School of Engineering Simats Department of BigData and Network Security Tamil Nadu Chennai India Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India School of Engineering and Technology Computer Science & Engineering Cmr University Karnataka Bengaluru India Department of Computer Science Tamil Nadu Perambalur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

This paper investigates how Green Cloud Computing may be made more efficient by combining LEED standards with Ant Colony Optimization (ACO), which is an approach to cloud computing that takes environmental considerations into account. The goal is to maximize resource allocation in data centers while minimizing energy use and carbon impact. The innovative combination of ACO and LEED offers a fresh perspective on achieving a balance between computational performance and environmental sustainability. The goal is to design a system that uses ACO-driven algorithms to optimize energy consumption in cloud infrastructure so it meets LEED standards. Innovative solutions for large-scale cloud settings are needed to address the challenges of scalability and convergence speed that ACO faces. Further, in order to widen the scope, Multi-Objective Optimization (MOO) and Particle Swarm Optimization (PSO) are investigated. While MOO has it tough dealing with competing goals, PSO has trouble striking a balance between the two. We can learn more about how to optimize cloud operations while reducing environmental effect thanks to innovations in these fields. By solving problems in ACO, PSO, and MOO, this study paves the way for Green Cloud Computing to become more efficient and less harmful to the environment. © 2023 IEEE.

关键词： Ant Colony Optimization Challenges Energy Environmental design Green Cloud Computing Innovations Leadership Resource Allocation Sustain ability Technology

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Optimizing Predictive Maintenance in Smart Factories Using Random Forests and Internet of Things Sensors 8

Optimizing Predictive Maintenance in Smart Factories Using R...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Preethi, E. Ahmed, A. Sarfaraz Shyamala, G. Vasukidevi, G. Sunil, Tummapudi Atheeswaran, Athiraja Rajalakshmi College of Engineering Rajalakshmi Nagar Thandalam Department of Computer Science and Design Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sr University Department of Civil Engineering Telangana Warangal India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Cse Medchal Malkajgiri India Hyderabad India

ISBN: (纸本)9798350367904

In the rapidly evolving landscape of Industry 4.0, smart factories leverage advanced technologies such as the Internet of Things (IoT) and machine learning to enhance operational efficiency. Predictive maintenance (PdM) is a critical aspect of this transformation, aiming to anticipate and mitigate equipment failures before they occur. However, existing PdM models often suffer from high false positive rates and inadequate scalability. This study addresses these issues by proposing an optimized predictive maintenance framework utilizing Random Forests and IoT sensors, tailored specifically for smart factory environments. Our methodology integrates a comprehensive IoT sensor network within the factory, collecting real-time data on various parameters such as temperature, vibration, and machine utilization. This data is preprocessed to handle noise and outliers, followed by feature extraction to identify the most significant predictors of equipment failure. We employ an optimized Random Forest algorithm, enhanced with a novel dynamic threshold adjustment technique and an adaptive learning mechanism to improve prediction accuracy and model robustness. The model's performance is evaluated using a large dataset collected from multiple manufacturing units over a period of six months. The proposed framework significantly outperforms traditional PdM approaches. Our optimized Random Forest model achieved an accuracy of 9 6. 8 %, a precision of 9 4. 5 %, and a recall of 9 7. 2 %, compared to 88.3 %, 85.7 %, and 89.1% respectively for the baseline models. Additionally, the dynamic threshold adjustment technique reduced the false positive rate by 40%, while the adaptive learning mechanism enhanced the model's ability to generalize. © 2024 IEEE.

关键词： Predictive maintenance

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Comprehensive asset surveillance and prediction modeling using real time data analytics 2

Comprehensive asset surveillance and prediction modeling usi...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rawat, Aayushi Gupta, Sumita Raghuvanshi, Gauri Aggarwal, Tanya Majhi, Vinayak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Amity Innovation and Design Center Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

The rapid evolution in the technology industry highlights the importance of developing a more advanced asset tracking system. The need for efficient and accurate asset management has become crucial and predominant across various industries. This paper displays the utilization of low-cost, and energy efficient technologies like Seeed Studio XIAO nRF52840 and Arduino Uno in designing and implementation of an asset tracking system. The design of the system primarily focuses on providing real time location of an asset, improving its tracking accuracy and operational efficiency. This study practically demonstrates the use of inexpensive microcontroller platforms along with BLE technology so as to develop an asset tracking system, the scalable solution that can be implemented across various industries such as logistics, healthcare, and manufacturing. © 2025 IEEE.

关键词： Costs

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep Learning 2

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Aditya Pai, H. Pareek, Piyush Kumar Guruprasad, M.S. Chandrappa, S. Bhargava, Krishna Mit Art Design and Technology University Mit School of Computing Pune India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Department of Computer Science and Engineering Dehradun India School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings. © 2024 IEEE.

关键词： Air quality

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Developing creative material in STEM courses using integrated engineering design based on APOS theory

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International Journal of Technology and design Education 2023年第4期33卷 1627-1651页

作者： Zhong, Hua-Xu Lai, Chin-Feng Chang, Jui-Hung Chiu, Po-Sheng Department of Engineering Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Computer and Network Center Department of Computer Science and Information Engineering Department of Computer Science National Cheng Kung University 1 University Rd. Tainan701 Taiwan Department of E-Learning Design and Management National Chiayi University No. 85 Wenlong Minxiong Township Chiayi County 621 Taiwan

Over the past several years, creativity has been recognized as an important skill for success in STEM education, engineering design and computational thinking. There is limited research on how to apply Conceive, design, Implement, Operate (CDIO) engineering design in STEM courses and how it affects students' creativity. This study strategically integrated computational thinking (CT), STEM, and CDIO engineering design, based on Action-Process-Object-Schema (APOS) theory, into a course. This study has adopted a framework to design creative learning materials and examined how this framework impacts students' creativity. This study used a pretest–posttest nonequivalent-groups design. The study investigated a STEM course which had 40 participants (n = 40, male = 11, female = 29) for 12 weeks. 6 weeks were spent using traditional teaching and 6 weeks integrating a CDIO framework into computational thinking. All the students in the course were college students with teacher college backgrounds. To examine the impact of our course, we used a questionnaire to examine the students’ creativity before and after the STEM course was applied. Our results show that our course had a significantly impact on our students’ creativity, particularly in the case of the male students. This finding is consistent with the findings of other research studies. This study also offers some suggestions for teachers who wish to improve their learning materials. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Students

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computer science Responses to Abuse Toward Women and Children 1

Computer Science Responses to Abuse Toward Women and Childre...

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1st International Conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Bendale, Bhagyashree Pramod Shirke, Swati Dattatraya MIT Art Design and Technology University MIT School of Computing Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350332803

Worldwide public health concerns include violence towards women as well as children. One in three women globally, according to estimates, has been the victim of physical, emotional, or sexual abuse. Like adults, one in two children experiences some type of violence every year, including bullying and domestic abuse. Women and children are increasingly more susceptible to additional forms of abuse including cyberbullying or emotional harassment because of the extensive use of the Internet and social media. In recent years, computer science and associated technologies have been used to assist address this societal issue. Solutions to identify and stop violence against women and children have been developed using cloud computing, pattern recognition, mobile computing, ubiquitous artificial intelligence, as well as associated systems. This study presents a comprehensive evaluation of some of the initiatives that might assist in addressing the issue of violence against children and women. The present state of these contributions is discussed in this study along with trends, designs, technologies, and open issues. A review of the scholarly literature from 2010 to 2020 was used to produce the survey. The contributions were categorized into four application domains: education, safety, offline detection, and online detection. Digital simulation, the Internet of Things, and artificial intelligence were employed as additional categories to further divide these contributions. © 2023 IEEE.

关键词： Internet of things

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