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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Varshney, Gunjan Ar-Reyouchi, El Miloud Shree, Bhagya Patel, Ankita Jss Academy of Technical Education Department of Electrical Engineering Noida India E.T.S Uned Department of Telecommunication and Computer Science Madrid Spain

ISBN: (纸本)9798350330724

As the demand for energy rises, photovoltaic (PV) system usage is increasing globally. Since PV systems are not linear, special methods are employed to generate the most amount of power. The energy generated by PV panels when sunlight is directly absorbed is not constant because of fluctuations in solar intensity. Artificial intelligence (AI) is one of the most powerful tools to track points with the most power. For the solar panels to provide the most electricity feasible, maximum power point tracking (MPPT) is employed. We have compared the performances of MPPT approaches control systems made up of a PV solar system in this research in order to decrease the energy loss supplied by the PV module and to boost energy efficiency. The MPPT, which is based on the Perturb and Observe algorithm (P&O), Fuzzy Logic algorithm (FL), and NARMA-L2 Controller algorithm, is used to create this control in the MATLAB/Simulink environment. To determine the most effective method., these three algorithms have been examined and their functional analysis performed. © 2023 IEEE.

关键词： Controllers

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Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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Variance-Guided Structured Sparsity in Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1714-1723页

作者： Pandit, Mohammad Khalid Banday, Mahroosh Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi110016 India Indian Institute of Technology Delhi Samsung Innovation Lab Bharti School of Telecommunication Technology and Management New Delhi110016 India

The success of deep neural networks, especially convolutional neural networks in various applications, has greatly been possible by the presence of an enormous number of learnable parameters. These parameters increase the learning capacity of the model, but at the same time, it also significantly increases the computational and memory costs. This severely hinders the scalability of these models to limited resource environments, such as IoT devices. The majority of the network weights are known to be redundant and can be removed from the network. This article introduces a regularization scheme, which is the combination of structured sparsity regularization and variance regularization. It simultaneously helps to obtain computationally sparse models by making the majority of parameter groups zero and increasing the variance of nonzero groups to compensate for the accuracy drop. We use sparse group lasso, group sparsity variant of 1 (lasso) regularization, which removes redundant connections and unnecessary neurons from the network. For variance regularization, the KL divergence of the current parameter distribution and the target distribution is minimized, which aims to have the concentration of weights toward zero and a high variance of nonzero weights (skewed distribution). To check the effectiveness of the proposed regularizer, the experiments are performed on various benchmark datasets and it is noticed that variance regularization helps to reduce the accuracy drop caused by sparsity regularization. On MNIST, the trainable parameters are reduced from 331 984 (baseline model) to 57 327 and managed to obtain better accuracy than the baseline (99.6%). Also, on Fashion-MNIST, Cifar-10, and ImageNet the proposed scheme achieved state-of-the-art sparsity with almost no drop in accuracy. © 2020 IEEE.

关键词： Deep neural networks

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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Advanced classified turning traffic volume module at Unsignalized Intersection 9

Advanced classified turning traffic volume module at Unsigna...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Kulkarni, Soham Deshpande, Varsha Kshirsagar Bhalerao, Raghavendra Dj Sanghavi College of Engineering Department of Information Technology Mumbai India Rmd Sinhgad College of Engineering Department of Electronics And Telecommunication Pune India Iitram Ahmedabad India Institute of Infrastructure Technology Research And Management Department of Electrical And Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350394474

Intersection study is one of the crucial surveys for advanced traffic management, especially at unsignalized points. In this study, the traditional method for vehicle detection and classification is initially implemented to classify traffic volume at unsignalized intersections. To enhance the accuracy of object detection and counting, a deep learning approach for vehicle classification and counting is designed using the YOLO (You Only Look Once) algorithm and a modified YOLO algorithm. This includes a variation of a statistical approach for the best outlier detection. The approach aims to improve the efficiency of vehicle classification and counting at unsignalized intersections, which is a critical task in intelligent transportation systems. The results verified for four classes of vehicles and indicate that the deep learning approach with an effective outlier detection approach increased the efficiency of vehicle classification and counting by 30 to 40 percent with satisfactory results. © 2024 IEEE.

关键词： Anomaly detection

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency 7

Optimizing Task Execution in Robotic Swarms: Coordination St...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bansla, Vandna Kaur, Inderjeet Bhutia, Rudramani Bisht, Girish Singh Sharma, Khemraj Shivalik Engineering College U.K Dehradun India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering U.P. Ghaziabad India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India Tula's Institute Department of Computer Science & Engineering Dehradun248001 India Nanhua University Program in Management Sciences Department of Business Administration Chiayi Dalin62248 Taiwan

ISBN: (纸本)9798350350067

The 'swarm robotics' is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Improving Flood Prediction with Deep Learning Methods

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Journal of The Institution of Engineers (India): Series B 2022年第4期103卷 1189-1205页

作者： Nayak, Monalisa Das, Soumya Senapati, Manas Ranjan Department of Electronics and Telecommunication Engineering Indira Gandhi Institute of Technology Sarang India Department of Computer Science Engineering Government College of Engineering Kalahandi India Department of Information Technology Veer Surendra Sai University of Technology Burla India

Flood is a natural calamity and is needed to be controlled in a specific manner. In this paper, flood forecasting is carried out using Deep Belief Network (DBN) for the banks of river Daya and Bhargavi that flows across Odisha, India. A comparative study is done using other machine learning techniques to elaborately show the impact of barrage construction. The performance of DBN is established by comparing it with Teaching Learning-Based Optimization method (TLBO) with the use of many parameters like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The flood forecasting is done for 1 day, 1 week and 2 weeks of time period using both the rivers, i.e. Daya and Bhargavi with the use of DBN and TLBO technique. © 2022, The Institution of Engineers (India).

关键词： Floods

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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