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

作者： Khushboo Singh V. Mahalakshmi Pranali G. Chavhan Prateek Garg Girija Shankar Sahoo Sukumar R Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This abstract discusses the capability of system learning to serve as a device for real-time facts evaluation and its ability to decorate the accuracy of the resulting insights. Specifically, it examines the advantages of computerized function engineering and the way system gaining knowledge of primarily based tools may be leveraged to offer businesses with actionable data insights extra efficaciously than conventional strategies. It additionally looks at a few rising technologies, inclusive of deep neural networks, and their capability to push the limits of information analysis in real-time, and also discusses how improvements in allotted computing can similarly supplement the impact of gadget studying gear. Eventually, this summary additionally affords some demanding situations associated with actual-time evaluation of massive volumes of facts, and some advised answers.

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Comparative study for different types of mppt algorithms using direct control method 1

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Controllers

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Detecting Obstacles within the Driving Lane and Vehicle Speed Adjustment

Detecting Obstacles within the Driving Lane and Vehicle Spee...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Ljepic, Srdan Grbic, Ratko Kovacevic, Jelena Krunic, Momcilo RT-RK Institute for Computer Based Systems Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia University of Novi Sad Faculty of Technical Sciences Novi Sad Serbia

ISBN: (纸本)9781665404174

Driving lane detection and collision avoidance are common Advanced Driving Assistant Systems in modern vehicles. In this paper the algorithm for obstacle detection inside driving lane and vehicle speed adjustment is proposed. The proposed algorithm processes an image from a front view camera using traditional computer vision algorithms such as color filtering, Canny edge detection and Random sample consensus (RANSAC) to detect driving lane and to detect possible obstacle inside the driving lane. The abovementioned algorithms are implemented in the C++ programming language using a OpenCV library. The estimation of the obstacle distance from vehicle as well as automatic braking is based on data obtained using Carla simulator. The evaluation of the proposed algorithm is performed on still images taken from Carla simulator to show the effectiveness of obstacle detection and distance estimation while driving simulations are performed in order to evaluate vehicle speed adjustment in case of an obstacle within the driving lane. © 2021 IEEE.

关键词： Cameras

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An Enhanced Deep Neural Network Enabled with Cuckoo Search Algorithm for Intrusion Detection in Wide Area Networks 5

An Enhanced Deep Neural Network Enabled with Cuckoo Search A...

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5th International Conference on information technology for Education and Development, ITED 2022

作者： Jimoh, Rasheed Gbenga Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Ojo, Stephen Akanbi, Mulikat Bola Bamigbaye, Jesufemi Ayotomide Faruk, Nasir University of Ilorin Faculty of Communication and Information Sciences Department of Computer Science Ilorin Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany Anderson University Department of Electrical and Computer Engineering AndersonSC29621 United States Kwara State Polytechnic Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Physics Nigeria SLU Directorate of ICT Nigeria

ISBN: (纸本)9781509064229

A diversity of harmful software has been created as a result of the dramatic increase in internet usage, posing major risks to computer security. There is a great probability that the numerous computing operations performed through the network will be interfered with or altered, and as a result, effective intrusion detection systems are imperative. In addition, the attacks on the network are unpredictable, something that emphasizes the value of creating effective classification and prediction models. Machine learning (ML) and Deep Learning techniques have been used to evaluate datasets for intrusion detection systems (IDS). The employment of the DL-based approach enabled by feature selection helps to address challenges with data quality, handling high-dimensional data, and other related issues. Therefore, due to the large nature and volume of the IDS datasets, and the ability of DL-based models to learn categories incrementally through their hidden layer architecture to produce more accurate results in big data, this study proposes a Long-Short-Term-Memory (LSTM) model, and to further enhance the classification capacity of the projected DL method, the cuckoo search algorithm was introduced to select optimal features from the wireframe. The accuracy and subsequent detection of the suggested model positive and negative rates were evaluated. The experimental results show that the LSTM outperformed some other existing models with the highest classification accuracy of 99.7% and an error rate of 0.006. © 2022 IEEE.

关键词： Long short-term memory

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POLYNOMIAL GROWTH AND PROPERTY RDp FOR ÉTALE GROUPOIDS WITH APPLICATIONS TO K-THEORY

arXiv

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arXiv 2023年

作者： Austad, Are Ortega, Eduard Palmstrøm, Mathias Department of Mathematics and Computer Science Faculty of Science SDU Odense University of Southern Denmark Odense Denmark Department of Mathematical Sciences Faculty of Information Technology and Electrical Engineering NTNU Norwegian University of Science and Technology Trondheim Norway

We investigate property RDp for étale groupoids and apply it to the computation of the K-theory of reduced groupoid L p-operator algebras. In particular, under the assumption of polynomial growth, we show that the K-theory groups for a reduced groupoid L p-operator algebra are independent of p ∈ (1, ∞). We apply the results to coarse groupoids and graph *** Codes 46L80, 47L10, 46L87 Copyright © 2023, The Authors. All rights reserved.

关键词： Computation theory

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An overview of water quality monitoring in IoT based Aquaculture

An overview of water quality monitoring in IoT based Aquacul...

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2021 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021

作者： Prapti, Dipika Roy Shariff, Abdul Rashid Mohamed Man, Hasfalina Che Ramli, N.M. Perumal, Thinagaran Shariff, Mohamed Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Malaysia Institute of Bioscience Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9781713833536

Internet of Things (IoT) based Aquaculture is an ongoing area of interest in the fisheries sector. The Agriculture 4.0 era is being advanced by IoT technology, yet the Aquaculture 4.0 is a lagging area in many countries. This article presents results obtained so far of an ongoing research of published work highlighting the monitoring of water quality in fishponds. This analysis was performed extensively from May to December 2020 on a total of 28 internationally published research papers. This review is divided into four categories: (1) recent research, (2) aquaculture environment (inland and marine), (3) research approaches for water quality monitoring, (4) common water quality parameters. Much of the published research concentrated on inland aquaculture (81%), while on marine aquaculture species accounted for 19% of papers reviewed so far. The experimental approach (64%) was the most widely practiced type of research in water quality monitoring. There is a need for long term experimental research to identify the challenges and suggest appropriate solutions. In terms of water quality parameters, temperature (35%), dissolved oxygen (34%), and pH (31%) are topmost prioritized water quality parameters. Future work will delve into more relevant papers and expand into the system architecture and the major components of IoT based aquaculture e.g., sensors, technology, data transmission pathway, and power supply. It is hoped that the findings from this review will support the aquaculture industry, researchers, practitioners, and decision makers in the long run. The authors welcome suggestions on relevant papers to be included in this ongoing review. © ASABE *** right reserved.

关键词： Internet of things

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zxCAPTCHA: New Security-Enhanced CAPTCHA

zxCAPTCHA: New Security-Enhanced CAPTCHA

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International Conference on Knowledge and Smart technology (KST)

作者： Nghia Dinh Trung Nguyen Vinh Truong Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 708 33 Ostrava Czech Republic Department of Information Technology FPT University Ho Chi Minh City Vietnam Faculty of Information Technology Ho Chi Minh City Open University Ho Chi Minh 722000 Vietnam

Automated attacks using CNN (Convolutional Neural Network), ML (Machine Learning), and DNN (Deep Neural Network have been successful in bypassing traditional CAPTCHAs. However, Deep Learning techniques, adversarial examples and style neural transfer, have been shown to be particularly effective in protecting CAPTCHAs. In this study, the authors proposed zxCAPTCHA, a new CAPTCHA that combines cognitive-based, image-based, and text-based CAPTCHA characteristics with Deep Learning techniques to improve security. Extensive evaluations were conducted to assess the improvement of the CAPTCHA security. The experiment shows that zxCAPTCHA considerably enhances the security while maintaining comparable usability. We also demonstrate the effectiveness of combining cognitive techniques and Deep Learning to improve CAPTCHA security.

关键词： Deep learning Neural networks Security Convolutional neural networks Usability CAPTCHAs

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning

Optimizing Inventory Management using Demand Forecasting bas...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： V. Pavithra Haayder M. Abbas Manjula B M P. K. Chidambaram Jothilakshmi S Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Information Technology Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF).

关键词： Accuracy Computational modeling Stacking Demand forecasting Inventory management Predictive models Market research Feature extraction Imputation Tuning

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Medical Image Transmission Using a Secure Cryptographic Approach for IoMT Applications 1

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International Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022

作者： Ravi, Renjith V. Goyal, S.B. Djeddi, Chawki Kustov, Vladimir Department of Electronics and Communication Engineering M.E.A Engineering College Kerala India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Lab Rouen University Rouen France Saint Petersburg Railway Transport University of Emperor Alexander I Saint Petersburg Russia

ISBN: (数字)9783031270994

ISBN: (纸本)9783031270987

Image security is very important in the field of medical imaging. To protect medical healthcare images, many studies have been undertaken. Because it eliminates data loss, encryption is the greatest solution for image secrecy. Traditional encryption methods, on the other hand, are difficult to apply to electronic health data due to data size limitations, redundancy, and scalability, especially when the data of a patient is delivered via different networks. As a result, since images are distinct from the text in terms of data loss and confidentiality, patients may loose the confidentiality and privacy of their data. Researchers discovered these security flaws and proposed different image encryption ways to remedy the problem. In order to build a safe image encryption solution for the healthcare business, this research proposes an efficient, lightweight encryption algorithm. The suggested lightweight encryption algorithm first separates the image into multiple clusters and then employs cluster-based permutations. Other procedures like diffusion and modulation could be used after that. The suggested technique is studied, analysed, and compared to generally encrypted ones in terms of information security and time complexity. The performance of the proposed method was assessed using a range of test images. Several experiments show that the proposed image cryptosystems methodology is more efficient than existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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A novel cancellable biometric recognition system based on Rubik's cube technique for cyber-security applications

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Optik 2023年 285卷

作者： Helmy, Mai El-Rabaie, El-Sayed M. El-Dokany, Ibrahim Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Objective: Ten years ago, biometrics became an efficient tool of security and authentication enforcement. In a classical access system, a user can easily change password if it is compromised. However, user's biometrics are limited and unique, and if a user biometric trait is compromised, it will be impossible to change it. Hence, cancellable biometric systems depend on the transformation of data or a biometric feature into a new format, so that users can replace their biometric templates in the same or a different system. In this paper, an efficient cancellable biometric recognition system is presented. In this system, both signal-and feature-level transformations are used to generate the cancellable templates Methods: A novel hybrid encryption framework based on the Rubik's cube technique is presented to allow efficient encryption of face, iris, and fingerprint biometric templates. This framework begins with extraction of biometric features using the Scale-Invariant Feature Transform (SIFT). After that, the resulting biometric features are encrypted with the optical Double Random Phase Encoding (DRPE) algorithm. Then, the resulting encrypted biometric features are further encrypted with the chaotic Baker map permutation, Advanced Encryption Standard (AES), or Ron's Code (RC6) algorithm in the Cipher Feed Back (CFB) mode of operation. Finally, the resulting encrypted biometrics are additionally encrypted with the Rubik's cube technique. The chaotic, RC6, or AES encrypted biometrics are utilized on the faces of the Rubik's cube. From the concepts of image encryption, the DRPE, RC6, and the AES algorithms introduce a degree of diffusion, whilst the chaotic Baker map adds a degree of permutation. Moreover, the Rubik's cube technique adds more permutation to the encrypted biometrics, simultaneously. The quality evaluation of the proposed cancellable biometric system reveals good performance. Results: The simulation results prove that the suggested hybrid encryption fram

关键词： Biometrics

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