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A Cross Design for Breast Cancer Prediction 1

6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Saini, Hemant Kumar Preeti Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Chandigarh University Punjab Gharuan India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

Breast cancer in women’s becoming the serious cause moving to the morbidity and the mortality worldwide. This paper aims to design the hybrid model using various machine learning classification algorithms like k-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), and Gaussian Naïve Bayes to predict the breast cancer. Moreover, the accuracy has been increased with the varied classifiers and decides the precision using the f1- Score, and Jaccard index. The data set is of Kaggle is used to train the model and then test them using the machine learning algorithms. The proposed model is also prepared forum foreseen data in future by validating using K-fold Cross Validation Technique. By Random Forest Classifier (RFC) model exhibit the significant performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Efficiency Unleashed by Examining Response Time Enhancement for Student Club Activities in iOS App and Web Application 5

Efficiency Unleashed by Examining Response Time Enhancement ...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Venkata Indrasena Reddy, S. Surendran, R. Logapriya, E. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The aim of this research is to analyze and enhance the response time of digital platforms, including both mobile apps and web applications, dedicated to student club activities. Materials and Methods: A sample size of N=12 is used for testing both the web and iOS applications. To guarantee 80% statistical power, sample sizes are calculated using G Power. Results: In contrast to the IOS application, which responds in 27.33 ms, the WEB application has a faster accuracy rate at 21.00 ms. Considering that the two have a p-value of 0.003 (p © 2024 IEEE.

关键词： Accuracy Statistical analysis Market research Mobile applications Time factors Intelligent systems Testing

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Design of electric powertrains to achieve NVH performance using autoencoders and a physical meaningful latent space

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Neural Computing and Applications 2025年第16期37卷 10287-10308页

作者： Ricardo Souza, Marcos Offner, Guenter Soltoggio, Andrea Mohammadpour, Mahdi Theodossiades, Stephanos Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom AVL List GmbH Graz Austria Computer Science Department School of Science Loughborough University Loughborough United Kingdom

One of the fundamental differences in the perception of electric (e-) vehicles is how their radiated noise is perceived with respect to classic internal combustion engines. Even though e-vehicles are usually quieter, the tonal content of the radiated noise can be more annoying. This paper proposes a novel approach that starts from the assumed radiated noise spectrum profile as input to a neural network that can return powertrain design parameters that would lead to generation of that specific noise profile. The proposed network acts as an autoencoder where the latent space is forced to have a physical meaning. As diverse combinations of powertrain parameters can result in similar noise profiles, a variational autoencoder is used to learn a structured latent representation, ensuring continuity and smooth transitions between possible solutions. The network predictions are validated against results of a three-dimensional CAE e-powertrain model. Overall, the mean absolute error is around 5 dBA for this feasibility study, which aims to demonstrate the concept. This work takes an inverse approach to the optimisation problem by starting from the user-perceived noise to predict the parameters required to achieve that. Although this study focuses solely on gear teeth microgeometry changes and bearing preloads, additional powertrain parameters could be incorporated as needed. © The Author(s) 2025.

关键词： Internal combustion engines

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A Comparative Analysis of Image Processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments 5

A Comparative Analysis of Image Processing and Deep Learning...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

关键词： Intelligent systems

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Towards Large-Scale Benchmark Dataset for Remote Sensing Object Detection on Battlefield

Towards Large-Scale Benchmark Dataset for Remote Sensing Obj...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Kim, Yechan Park, JongHyun Kim, Sihyun Kim, Sungheon Kim, SooYeon Ko, YeongMin Oh, Junggyun Jeon, Moongu School of Electrical Engineering and Computer Science GIST Korea Republic of Kyungpook National University Department of Robot and Smart System Engineering Korea Republic of HDC Labs Korea Republic of

ISBN: (纸本)9798331530839

Precise object detection allows military personnel to clearly understand their surroundings, leading to planning effective military strategies. Particularly, satellites and drones allow real-time surveillance over large areas, which is crucial for military operations. Collaboration among military, academic, and industrial institutions is required to promote innovation in military target detection. However, potential security concerns usually restrict access to real data from the military. To mitigate this issue, this paper proposes a novel approach to generate synthetic data for remote sensing object detection on the battlefield with ARMA3, one of the renowned military tactic games. With our method, the data for model training can be easily generated without any manpower. To demonstrate the efficacy of our approach, we provide a detailed analysis of the examples from our method. As ARMA3 is well-known for its realistic military combat simulation, we believe our method can effectively contribute to military object detection in remote sensing. © 2024 IEEE.

关键词： Military satellites

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A Novel Approach for Sign Language Video Generation Using Deep Networks 2

A Novel Approach for Sign Language Video Generation Using De...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Kumar, Sachin Deepa, B. Kavitha, T. Tamilselvi, M. Sathiyapriya, V. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srmist Ramapuram Campus Department of Electroncis and Communication Engineering Ramapuram India MA.M College of Engineering and Technology Department of Computer Science and Engineering Trichy India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India School of Computer Science and Engineering Vellore Institue of Technology Chennai Campus Chennai India

ISBN: (纸本)9798350373110

Sign language enhances the communication capabilities of the deaf-mute community, allowing for a deeper understanding of their needs and emotions. These languages are highly structured and visual, using gestures and various upper body movements such as those of the hands, face, eyes, and gaze. Researchers face numerous challenges in recognizing and translating the diverse variations in sign movements, which requires specialized expertise in computer vision and artificial intelligence. Sign language recognition and translation research has garnered global attention. This research work introduces a novel methodology for generating sign gesture videos from text inputs by integrating various intelligent techniques. The proposed model employs an enhanced generative adversarial network (GAN) to create sign videos from input sentences. Experiments with the proposed VideoGAN model using diverse sign language datasets from multiple countries have demonstrated its effectiveness. The research outcomes highlight its contribution to high-quality video production, with improved evaluation metrics underscoring the model's superior performance. © 2024 IEEE.

关键词： Generative adversarial networks

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of Electronics and Information Engineers.

关键词： Cavity resonators

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Analyze the impact of feature selection techniques in the early prediction of CKD

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

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Enabling Technologies for Effective E-Waste Management 2nd

Enabling Technologies for Effective E-Waste Management

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2nd International Conference on Emerging Trends in engineering and Technology, EGTET 2022

作者： Abdullah, Ezan Hilal, Khushaima Department of Computer Science and Engineering School of Engineering Science and Technology Jamia Hamdard New Delhi110062 India

ISBN: (纸本)9789819943616

Effective or efficient management of e-waste is considered as preeminent vital challenges of the modern days. The massive scale of e-waste generated and dumped in open landfills or oceans without proper treatment poses severe threat to the environment around the globe. E-waste can be considered as one of the prime root causes of different types of pollution like air, soil, and water. Furthermore, the absence of stringent rules and regulations for proper e-waste treatment, duping, and management also adds to the problem. With these issues as a motivation factor, this paper proposes an efficient e-waste management framework for effectively managing the e-waste. The paper discusses the various issues and challenges of e-waste management. Furthermore, the role of various enabling technologies like IoT, WSN, blockchain, and artificial intelligence in effective e-waste management is also discussed. Finally, some of the best practices and future research directions are proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Machine learning

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Scalable alignment-free feature extraction approach for genome data and their cluster analysis

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Multimedia Tools and Applications 2025年 1-37页

作者： Tripathi, Abhishek Tiwari, Aruna Chaudhari, Narendra S. Ratnaparkhe, Milind Bharill, Neha Jha, Preeti Dwivedi, Rajesh Department of Computer Science IIT Indore Simrol Madhya Pradesh Indore453552 India ICAR-Indian Institute of Soybean Research Indore Madhya Pradesh Indore452001 India Department of Computer Science Mahindra University Ecole Centrale School of Engineering Telangana Hyderabad500043 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad500043 India

Feature extraction is crucial in bioinformatics, as it converts genomic sequences into numerical feature vectors essential for machine learning algorithms, particularly in clustering, to identify the families of newly sequenced genomes. Traditional methods have relied on alignment-based techniques for clustering the genomic sequences. However, these methods are computationally intensive. In contrast, alignment-free methods are now more commonly used due to their reduced computational demands. Despite this, many alignment-free approaches may generate identical feature vectors for dissimilar sequences, as they focus solely on single nucleotide counts (1-gram) and their arrangement during feature extraction, often neglecting dinucleotide counts and their arrangement, which can degrade clustering performance. Furthermore, certain approaches include trinucleotide or higher-order compositions;they introduce high-dimensionality issues, resulting in inaccurate results. Additionally, some existing methods are not scalable and take substantial time to extract features from large genomic sequences. To address these issues, we proposed a novel 33-dimensional Scalable Alignment-Free Feature Vector (33d-SAFFV) approach to extract the significantly important features such as length of sequence, count of dinucleotides, and positional sum of dinucleotides, which produces a 33-dimensional feature vector. This approach leverages Apache Spark for scalability and efficient in-memory computations, making it suitable for large datasets. We evaluated the performance of our proposed method by applying the extracted 33-dimensional feature vectors to K-Means and Fuzzy C-Means (FCM) clustering algorithms. Performance is measured using the Silhouette Index (SI) and Calinski-Harabasz (CH) index. Experimental results on the gene sequences of four varieties of rice datasets and two varieties of soybean datasets show the effectiveness of the proposed 33d-SAFFV approach. In K-Means clustering with t

关键词： Nucleotides

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