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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Kumar, Indradeep Dasari, Madhavi Danamaraju, Chaitanya Bindu, K.V. Mohanavel, V. Dhanraj, Joshuva Arockia Institute of Aeronautical Engineering Department of Aeronautical Engineering Dundigal Hyderabad500043 India Sapthagiri College of Engineering Department of Computer Science Karnataka Bengaluru560057 India RNS Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru560098 India R.M.K. College of Engineering and Technology Department of Science And Humanities Tamil Nadu Thiruvallur601 206 India Bharath Institute of Higher Education and Research Centre for Materials Engineering and Regenerative Medicine Tamil Nadu Chennai600073 India Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9798350395235

Lithium-ion (Li-ion) batteries are the preferred choice for electric vehicles (EVs) because of their extended lifespans, low self-discharge rates, high voltage, and high energy density. A well-functioning Battery Management System (BMS) is critical to the efficient operation of an EV. The State of Charge (SoC) is an important statistic that reflects the remaining charge in the battery, and its exact assessment is essential for BMS and improving EV efficiency, which extends the battery's life and decreases the probability of catastrophic failure. However, SOC estimation is complicated and affected by numerous unknowns, such as battery age and external temperature. In this study, we estimated SOC using a Convolutional Neural Network (CNN) model. To improve the CNN architecture, this study has applied three different optimization algorithms: Particle Swarm Optimization (PSO), Elephant Search Algorithm (ESA), and Equilibrium Optimization (EO). Sensor data from lithium-ion batteries were carefully processed. The processed dataset was then supplied to the CNN and three optimized CNN models. These models were tested using error, R2, and time metrics to identify the optimal technique. CNN-ESA outperformed the other CNN models in SOC estimation, with the lowest error rates and the highest R2 value of 0.9987. This simulation result demonstrates the effectiveness of applying ESA to improve CNN architectures for better Li-ion battery SOC estimates. It enhances the efficiency and lifespan of EVs. © 2024 IEEE.

关键词： Mean square error

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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Fraud Detection and Prevention for a Secure Financial Future Using Artificial Intelligence 6

Fraud Detection and Prevention for a Secure Financial Future...

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Chaudhry, Rishi Kaur, Sandeep Singla, Jagdeep Mittal, Ruchi Malik, Varun Institute of Management Studies and Research Maharshi Dayanand University Haryana Rohtak India Guru Nanak Dev University Department of Computer Engineering and Technology Punjab Amritsar India HPKV Business School Dhauladhar Parisar-II Central University of Himachal Pradesh Dharamshala Distt HP Kangra India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350306613

The demand for reliable and intelligent solutions to identify and prevent financial fraud is growing in proportion to the complexity and sophistication of this crime. This article outlines a comprehensive plan to improve financial security via the use of state-of-the-art AI techniques. The suggested architecture consists of three main parts: a K-Means clustering for dataset preparation, an extended deep Q network (EDQN) for analysis and prediction, and a principal component analysis (PCA) for feature selection. An enhanced K-Means clustering method that can manage complex and large-scale financial data is our primary contribution. A key motivation for this strategy is the hope that it would facilitate the detection of legitimate and fraudulent activities by making it simpler to see clear patterns in the data. By streamlining the grouping process and producing more accurate clusters, a more efficient clustering technique improves the overall effectiveness of fraud detection. In the aftermath of getting the dataset ready, PCA feature selection follows. By lowering the dataset's dimensionality without sacrificing any of the valuable information, principal component analysis (PCA) finds and retains the most relevant characteristics. This guarantees that the chosen qualities greatly aid in the identification and prevention of fraudulent transactions, while also improving the computational efficiency of future activities. Lastly, an EDQN is used in a system for classification and prediction. By including characteristics that enable the model to comprehend more complex links in financial data, the EDQN enhances previous Deep Q Networks. © 2024 IEEE.

关键词： Artificial Intelligence Financial Security Fraud Detection K-Means Clustering

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview 16

Automated Recognition of Structures in Scanning Electron Mic...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Marascu, Valentina Mihailescu, Marius Iulian Nita, Stefania Loredana Rogobete, Marius 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania 'Ferdinand i' Military Technical Academy Department of Computers and Cybersecurity Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified. © 2024 IEEE.

关键词： Python

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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DERAINIZER for Unpaired Single Image De-raining

DERAINIZER for Unpaired Single Image De-raining

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2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Fernando, Hasal Ayoob, Mohamed Poravi, Guhanathan University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9781665425032

Rain streaks are known to cause many performance or accuracy reductions in many computer vision systems. The problem of obstructing rain streaks is addressed within the domain of 'De-raining'. De-raining is the process of removing rain streaks. It has two main sub-categories which are video de-raining and single image de-raining. Many works on single image de-raining have taken place using both traditional approaches as well as deep learning approaches. This paper does a critical analysis of credible, novel, and best performing single image de-raining systems. Also, the paper addresses an unpaired training gap which exists within the domain with the novel de-raining system 'DERAINIZER', enabling all future researchers to employ unpaired training data to train their models. It is followed by results, research limitations and possible future works. © 2021 IEEE.

关键词： computer vision

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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Optical Skyrmionic Hopfion Induced Surface Structures in Azopolymers via Photo-Induced Mass Transport

Optical Skyrmionic Hopfion Induced Surface Structures in Azo...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Rihito Tamura Praveen Kumar A. Srinivasa Rao Natalia M. Litchinitser Takashige Omatsu Graduate School of Engineering Chiba University Japan Department of Physics Indian Institute of Technology Bhilai India Molecular Chirality Research Center Chiba University Japan Institute for Advanced Academic Research Chiba University Japan Department of Electrical and Computer Engineering Duke University USA

We report on the formation of surface relief structures of azopolymers by irradiation of optical skyrmionic hopfion. The reliefs reflect exotic polarization textures of hopfion and will pave the way for advanced rewri... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Radiation effects Optical polarization Optical device fabrication Surface emitting lasers Memory Lasers and electrooptics Surface structures

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Traffic Classification in Software-Defined Networking (SDN): Application of Machine Learning Models for Flooding DDoS Attack Detection 3

Traffic Classification in Software-Defined Networking (SDN):...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bosi, Lucas Leal Mendes, Andre Chaves Guedes, Raphael Melo Salles, Ronaldo Moreira Defense Engineering Military Institute of Engineering Rio de Janeiro Brazil VALORIZA - Research Centre for Endogenous Resource Valorization Campus Politécnico Portalegre7300-555 Portugal State University of Rio de Janeiro Campus Maracanã Informatics and Computer Science Department Rio de Janeiro Brazil CIICESI ESTG Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798331540319

Among the advantages offered by the SDN paradigm over traditional networks is the centralized implementation of comprehensive security policies. From this perspective, this study develops, trains, and tests several machine learning algorithms for traffic classification, distinguishing between legitimate traffic and three types of DDoS flooding attacks (HTTP, UDP, and TCP). The dataset used for training was employed both in its raw and preprocessed forms, and was generated in a reference study. The Random Forest and Gradient Boosting algorithms achieved the highest results in terms of accuracy and F1-score. Additionally, a comparison of the results of the studies is conducted. © 2024 IEEE.

关键词： Contrastive Learning

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