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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Pradeepkumar, G. Ramesh, V. Karthika, A. Karthikeyan, A. Sacithraa, R. Vanitha, K. Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India K.S.R College of Engineering Department of Computer Science and Engineering Tiruchengode India Surya Engineering College Department of Electronics and Communication Engineering Erode India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India K S R Institute for Engineering and Technology Department of Electrical and Electronics Engineering Tiruchengode India Velalar College of Engineering and Technology Department of Medical Electronics Erode India

ISBN: (纸本)9781665491990

City dwellers freauently visit shopping malls to browse merchandise. Weekends and holidays are the busiest days of the week for retail establishments. Because of the discounts and deals, there is a sense of urgency. People's shopping carts are filled with a variety of items. Customers must first choose their desired items before paying for them at the cash register. Installing the bill victimization bar code scanner at the billing counter takes time, which may result in customers having to wait for an extended period of time. People spent less time in checkout lines as a result of this study. Similarly, the intelligent shopping cart makes it simple to see the price of each item in the cart. After paying with the app or at the register, customers can take their purchases with them. The hardware components that allow it to function are the Arduino Nano, RFID Card, Reader Module, and Buzzer. Shopping is more efficient if items are not scanned at the register. This method helps clients estimate the total cost of an item. As a result, the shopper can only spend money on necessities. RFID has helped to reduce billing system errors, which cost a lot of money. © 2023 IEEE.

关键词： Sales

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Relative Power Loss Analysis of Poly-Si PV Panels: An Overview in Eastern Indian Climatic Condition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1775-1780页

作者： Pal, Sudipta Basu Das Bhattacharya, Konika Mukherjee, Dipankar Department of Computer Science Technology University of Engineering Management Kolkata India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Department of Electronics and Telecommunication Engineering Indian Institute of Engineering Science and Technology Shibpur India

Renewable energies are generated from natural processes that are continuously renewed. Therefore, it is required to combine the PV modules either in series combinations or parallel combinations for developing the appropriate Photovoltaic (PV) panel/PV arrays. Several PV modules are connected in a series–parallel combination to constitute PV arrays. These PV arrays currently act as a decentralized source of D.C power for onward transmission. When identical wattages PV modules are coupled in a parallel or series connection, there must be a probability to the PV module's manufacturing process that gives rise to power loss. Mainly these types of losses are called Mismatch losses. Mismatch losses (power losses) in PV panels principally occur in terms of Relative Power Loss (RPL). Therefore, the authors carried out I–V characterizations of different technology of PV modules under varying insolation and temperatures. The main aim of the work is to develop the right kind of photovoltaic (PV) panel/PV arrays in a particular Eastern Indian climatic zone. From the authors' characterization of RPL, analysis was conducted to judge which technology is most suitable to build a large size array/BIPV. The experimental results reveal lower mismatch losses of poly-Si PV modules for crystalline silicon and amorphous silicon counterparts. © 2022, The Institution of Engineers (India).

关键词： Solar panels

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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A Hybrid Classification Framework of Centrifugal Pumps Using Wavelet Coherence Visuals and Principal Component Analysis 25

A Hybrid Classification Framework of Centrifugal Pumps Using...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Ullah, Niamat Siddique, Muhammad Farooq Kim, Jong-Myon Electronics and Computer Engineering University of Ulsan Department of Electrical Ulsan Korea Republic of University of Ulsan Electronics and Computer Engineering Pd Technology Co. Department of Electrical Ulsan Korea Republic of

ISBN: (纸本)9798350330014

Centrifugal pump fault detection and isolation in an early stage is essential from an industry and consumer viewpoint for significantly reducing downtime and maintenance activities., resulting in cost savings. This paper presents a comprehensive framework for centrifugal pump fault diagnosis integrating wavelet coherence analysis and hybrid classification. The proposed framework consists of several modules aiming to attain accurate fault diagnosis. Firstly., the National Instruments-based data acquisition module acquires the vibration signals from the centrifugal pump for healthy baseline and fault-defected signals. A low-pass filter amplifies the fault-specific signal. We perform the Wavelet coherence analysis on the amplified healthy baseline and fault-defected amplified signals to assess the correlation and coherence among different frequency components., resulting in a Wavelet Coherence Visual (WCV) image. Gaussian filtering is applied directly to the WCV., reducing noise and enhancing data quality. Principal Component Analysis (PCA) reduces dimensionality., refining the representation of pump health and fault characteristics. In the classification stage., a tailored hybrid approach., combining t-Distributed Stochastic Neighbor Embedding (t-SNE) and K-Nearest Neighbors (KNN)., significantly enhances the precision of centrifugal pump fault diagnosis and is responsible for categorizing fault classes into specific categories such as healthy., mechanical seal scratch., mechanical seal hole., and impeller defect. Achieving an impressive accuracy of 95.23%., KNN ensures accurate categorization based on health-sensitive WCV features., while t-SNE aids in visualizing data clusters., improving fault interpretability and predictive maintenance reliability. This approach significantly contributes to developing intelligent maintenance strategies., ultimately enhancing the reliability and performance of industrial pump systems. © 2023 IEEE.

关键词： Failure analysis

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Augmented hunger games search algorithm using logarithmic spiral opposition-based learning for function optimization and controller design

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Journal of King Saud University - engineering Sciences 2024年第5期36卷 330-338页

作者： Izci, Davut Ekinci, Serdar Eker, Erdal Kayri, Murat Department of Electronics & Automation Batman University Batman72060 Turkey Department of Computer Engineering Batman University Batman72100 Turkey Vocational School of Social Sciences Mus Alparslan University Mus Turkey Department of Computer and Instructional Technology Education Van Yuzuncu Yil University Van Turkey

This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and controller design. To assess the performance of the algorithm for function optimization, benchmark functions from the CEC2017 test suite were employed and comparisons were made with available and good performing algorithms. In terms of controller design, the proposed LsOBL-HGS algorithm was utilized to design a FOPID controlled magnetic ball suspension system. Comparative assessments were also performed for FOPID controller design, as well using other state-of-the-art methods reported for the magnetic ball suspension system. The results showed that the proposed LsOBL-HGS algorithm has good capability for FOPID controller design employed in a magnetic ball suspension system as it provided an improvement of more than 13% in terms of the transient response-related parameters and more than 34% in terms of bandwidth compared to the best-reported approach used for comparisons. © 2022 Karabuk University

关键词： Controllers

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A Review on Malware Analysis for IoT and Android System

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SN computer Science 2023年第2期4卷 1-45页

作者： Yadav, Chandra Shekhar Gupta, Sangeeta STQC Ministry of Electronics and Information Technology Delhi India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

Today all humankind is willing to avail more facilities and hopes everything should be available with a click of the button. In order to offer different services, the developers have come with inbuilt modules of several systems. This make easy system develoment and services may be offered intantly. These services are connected to the internet and accessible via Android phones and IoT devices. But this inbuilt module suffers from a lot of vulnerabilities, bugs, and default settings which may be difficult to change, as happened at the time of changing the password of home-based Wi-Fi router, which require external applications and OTP verifications, etc. Due to these issues and new hacking tools and techniques, security is a major challenge today. The basic framework to provide adequate security of the system comprises five following principles: integrity, confidentiality, availability, privacy, and nonrepudiation. The attacker may leverage advantage of any shortcomings that may lead to several issues. This work explores the cause of threads/vulnerability particularly for IoT, IIoT, SCADA, and Android application systems. The structure of this work is divided in different sections like, a short introduction to Malware, how it infects the system, and a detailed malware exploitation plan that is generally followed by expert attackers to exploit the vulnerabilities related to critical infrastructure or to defame the organization or countries is presented. In addition, General framework based introduction on IoT and Android is also presented with common vulnerabilities at every stage and respective mitigation strategies. Both static and dynamic analyses are evaluated in this work. It is identified that, for a better model design and evaluation, both are highly recommended for the implementation of effective malware detection strategies. Along with these models in order to protect the infra-structure Honeynet, IDS, IPS, Hardware-based securities like CPU and Memory and for

关键词： Android IDS IIoT IoT IPS Malware SCADA Virus

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The Application of Machine Learning Algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Gurumoorthy, Sasikumar Avikkal, Saritha Rani, M. Radha, K. Rajesh, N. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (ADHD). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. Despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of ML models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Electroencephalography

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Performance Analysis of Discrete Wavelet Transforms for Acoustic Scene Classification with DCASE Dataset 1

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2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Singh, Vikash Kumar Sharma, Kalpana Sur, Samarendra Nath Department of Computer Science and Engineering Sikkim Manipal Institute of Technology SMU Sikkim Majitar India Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology SMU Sikkim Majitar India

ISBN: (数字)9783031691461

ISBN: (纸本)9783031691454

To control the chaos during an acoustic scene, it is crucial to recognize each particular sound class in the surrounding. This paper discusses the development of a model for acoustic scene classification, incorporating Fast Dynamic Time Warping (DTW) and reference audio. The main goal of the model is to correctly classify various sounds that are produced in environments, including airport, metro stations, parks, public square, bus etc. The model successfully identifies similarities across the extensive dataset of 23035 audio samples gathered from the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge Task 1 by employing the Fast DTW approach. In light of the significance of handling extensive audio data, the integration of reference audio diminishes processing complexity. Additionally, the suggested model investigates the effectiveness of features from several Discrete Wavelet Transforms, including Meyer, Symlets, and Daubechies, to improve classification accuracy. The acquired results, which demonstrate the model’s efficacy and breakthroughs in acoustic scene classification, are compared with the DCASE 2021 baseline model and other technical papers presented in the same year. With the Meyer wavelet characteristic of audio data, the suggested model attains an accuracy of 79.92%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Subway stations

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Discriminative Dimension Selection for Enhancing the Interpretability and Performance of Clustering Output 16

Discriminative Dimension Selection for Enhancing the Interpr...

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16th International Conference on Knowledge and Smart technology, KST 2024

作者： Lian, Hrang Kap Waiyamai, Kitsana Konstantinos, Slavakis Haruechaiyasak, Choochart Kasetsart University Department of Electrical Engineering Bangkok Thailand Kasetsart University Department of Computer Engineering Bangkok Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Tokyo Japan Artificial Intelligence Research Group National Electronics and Computer Technology Center Bangkok Thailand

ISBN: (纸本)9798350370737

Discriminative Dimension Selection (DDS) has emerged as a powerful tool for identifying the most relevant features in high-dimensional datasets, enabling interpretable data analysis and visualization. This paper explores the application of DDS which utilizes overlapping clusters and dimensions to enhance the interpretability and performance of the K-means clustering algorithm. Our approach leverages the post-processing capabilities of K-means to selectively retain informative features and discard redundant or irrelevant ones. This refined feature set not only preserves the clustering performance of K-means but also enhances its interpretability and visualization. We demonstrate the effectiveness of our method using a variety of datasets and compare its performance against traditional K-means and the proposed method Overlap-resolved Clustering (ORC). We also test with multiple cluster validity indices such as the Silhouette Coefficient Score (SS), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). Our results consistently show that ORC produces better clustering results and enhances the interpretability of the datasets. This study highlights the potential of DDS as a valuable tool for improving the interpretability and visualization of high-dimensional data analysis using K-means clustering. © 2024 IEEE.

关键词： Visualization

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Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

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International Journal of Information technology (Singapore) 2024年第8期16卷 4915-4929页

作者： Ramani, R. Dhinakaran, D. Edwin Raja, S. Thiyagarajan, M. Selvaraj, D. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection MapReduce Normalization Regression analysis

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