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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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An approach Human Resource Information Systems (HRIS): A Hierarchical Deep Learning Framework for Predicting Employee Satisfaction

An approach Human Resource Information Systems (HRIS): A Hie...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Hannah, V. Betsy Tripathi, Pijush Kanti Elizarova, Lidiya Belidhe, Sandeep Rathnamma, M.V. Dineshkumar, P. St. Peter's College of Engineering and Technology Department of Master of Business Administration Tamilnadu India Haldia Government College Purba Medinipur Department of Geography West Bengal India Kuban State University Krasnodar Russia DevSecOps Telangana India Klm College of Engineering for Women Department of Computer Science and Engineering Andhra Pradesh Kadapa India Sona College of Technology Department of Information Technology Tamilnadu Salem India

ISBN: (纸本)9798331508685

The change from traditional techniques of human resource information management to urban computerization approaches was received with discontent by certain government personnel. Still, in this era of computerized governance, this shift is crucial since it improves the efficiency and effectiveness of human resource tasks. In light of this emerging tendency, the government has developed an HRMIS program specifically for use by government agencies. Human Resource Management Information System (HRMIS) is an integrated e-government service that acts as a public sector database and gives government workers a unified interface to do HR-related tasks. The suggested approach consists of two parts: preprocessing and training the model. Transferring data from an unstructured format to one that machine learning systems can work with is the goal of this phase. Data encoding and cleaning are part of the process, as is principal component analysis (PCA) for dimensionality reduction. It was the HDL that we used for training the model. We outperform state-of-the-art alternatives like CNN and DNN with our proposed model, which obtains an average accuracy rate of 93.58 percent. © 2025 IEEE.

关键词： Dimensionality reduction

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions

arXiv

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arXiv 2024年

作者： Eli, Aimina Ali Ali, Abida Master of Science in Information Technology School of Science & Technology Emporia State University Emporia KS USA Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Medical image analysis has emerged as an essential element of contemporary healthcare, facilitating physicians in achieving expedited and precise diagnosis. Recent breakthroughs in deep learning, a subset of artificial intelligence, have markedly revolutionized the analysis of medical pictures, improving the accuracy and efficiency of clinical procedures. Deep learning algorithms, especially convolutional neural networks (CNNs), have demonstrated remarkable proficiency in autonomously learning features from multidimensional medical pictures, including MRI, CT, and X-ray scans, without the necessity for manual feature extraction. These models have been utilized across multiple medical disciplines, including pathology, radiology, ophthalmology, and cardiology, where they aid in illness detection, classification, and segmentation tasks. This study offers a thorough examination of applications for deep learning in medical image processing, addressing both its achievements and obstacles. It underscores the relevance of deep learning in enhancing diagnostic precision in tasks including tumor detection, organ classification, and disease progression prediction. Additionally, we investigate sophisticated deep learning methodologies, such as generative adversarial networks (GANs) for picture creation and augmentation, alongside multimodal and federated learning for the integration of heterogeneous data sources and the improvement of model generalizability. Notwithstanding the encouraging outcomes, the domain continues to encounter obstacles like the necessity for extensive labeled datasets, model understanding, and regulatory impediments. We examine methods to tackle these issues and propose future avenues to enhance the influence of deep learning in clinical practice, especially regarding ethical considerations and real-time implementation. As deep learning advances, its incorporation into healthcare is poised to augment medical picture analysis and boost patient outcomes wo

关键词： Convolutional neural networks

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Exploring the predictive potential of artificial neural networks in enhancing mechanical properties of derivatives of graphene nanocomposites: a data-driven approach

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International Journal of Information technology (Singapore) 2025年第2期17卷 999-1005页

作者： Rao, D. Nageswara Dahiya, Mamta Arora, Amit Kumar Jandwani, Paras Verma, Gunjan CSE ACED Alliance University Chikkahagade cross Anekal Bangalore India MAIT Maharaja Agrasen University Himachal Pradesh Solan India Computer Science and Engineering IIT Madras Chennai India Accenture LLP Philadelphia PA United States Dept. of Master of Computer Applications G L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India

The given manuscript delves into the realm of materials science and engineering, specifically focusing on the utilization of different derivatives of graphene reinforced in matrix material (DGMM’s) to enhance mechanical properties. Graphene (Gr), with its remarkable characteristics, holds immense promise as reinforcement in composites and nanocomposites (NCs). Using linear regression and artificial neural networks (ANN) framework, this study predicts the weight% of enhanced mechanical properties in DGMMs. By analyzing a dataset comprising various graphene derivatives, matrix materials, processing methods, and resulting enhancements, the ANN model offers valuable insights into the intricate relationships within these NCs. The findings reveal the potential of ANN models in optimizing the design and fabrication processes of DGMMs, thereby accelerating materials development and enabling tailored formulations for specific applications. Also, the given manuscript motivates for future research to enhance the predictive accuracy and broaden the scope of applications for *** confirm the corresponding author is correctly identified and amend if ***, it is *** confirm the inserted city name is correct in Affiliation (3, 4) and amend if ***, it is correct and confirmed © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural networks Graphene Linear regression Nanocomposites Nanomaterial

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Design and Fabrication of Multiband Planar Antenna for 5G and Next-Generation Mobile Handset Applications

Design and Fabrication of Multiband Planar Antenna for 5G an...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Mahendrakumar, S. Kiruthika, S. Vijayakumar, P. Karthikeyan, G. Bharathi, V. Vanitha, K. Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of M.Tech - Computer Science and Engineering Erode Thudupathi638057 India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy Thottiyam India Velalar College of Engineering and Technology Department of Medical Electronics Erode India

ISBN: (纸本)9798350313987

Microstrip patch antennas, also known as planar antennas, have been widely used for several decades. To boost the efficacy of wireless technology, multi-resonance antennas must be used. Patch antennas suffer from a relatively narrow bandwidth. The proposed technology makes use of a small strip-patch antenna with a wide range of resonant frequencies. The proposed device can detect and capture signals at frequencies ranging from 1.6 to 3.2 gigahertz. This research study describes a small multi-broadband planar antenna that is suited for WLAN/WiMAX and LTF/GS M/UMTS mobile device integration. GSM850/900, WLAN/WiMAX, DCSIPCSIUMTS, and LTE2300/2500 frequency bands are covered by the aforementioned antenna. A monopole is present on an individual surface of the thin dielectric substrate, manifesting in a configuration that resembles the alphabets C and U. On the other side, there is a F-shaped stretch that is devoid of lumped parts. The antenna measures 15 mm long, 68 mm wide, and 0.8 mm thick, making it ideal for insertion into ultra-thin mobile devices. HFSS was used to simulate multiple parameters during antenna building, including radiation pattern, S-parameter, efficiency, peak gain, and return loss. The recommended antenna was built and tested using a vector network analyzer. The proposed antenna has the lowest return loss across all resonance frequencies, uses fewer phone antennae, and takes up less space. © 2023 IEEE.

关键词： 5G Microstrip Patch Antenna Mobile Handset Applications WLAN/WiMAX

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Neural Network Implementation for Battery Failure Detection in Electric Vehicles

Neural Network Implementation for Battery Failure Detection ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Vijitha, S. Hebri, Dheeraj Singh, Sangeeta Manohara, M. Ishrat, Mohammad Joseph, D Raja Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai600117 India Srinivas Institute of Technology Department of Master of Computer Application Karnataka Mangaluru574143 India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201301 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Hindustan Institute of Technology and Science School of Aeronautical Sciences Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350319200

Soil destruction and global warming have recently come to the forefront of discussion in industrialised nations as governments strive to accommodate the rising demands of their citizens. The demand for zero-emission electric cars has increased as a result of international competitiveness and technological advancements (EVs). Concerns about the high voltage of increasing numbers of electric cars are shared by an increasing number of individuals. Since the system of batteries may be at responsible for over 30% of EV accidents, it is vital to investigate how problems with LIBs are recognized. Many different kinds of problems make it hard to fix EV's LIB. Fast and precise diagnosis of battery pack problems is crucial for the immediate and ongoing safety of EV operation. Utilizing models of neural networks like multiple hidden layers (MLP) or nonlinear activation functions, this research provides a mechanism for identifying and fixing problems with electric vehicle batteries (RBF). To generate information for the BFD system, battery simulations are done in MATLAB. Accuracy may be improved by performing pre-processing steps on the information once it has been generated. After training, the two models are put to the test to see how well they perform. There are both positive and negative measures that may be used to determine which model is the best. © 2023 IEEE.

关键词： Accuracy Battery Electric Vehicle Fault Model Neural Network

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OHMBC: Optimized Hybrid deep learning Model for Classification of Breast Cancer

OHMBC: Optimized Hybrid deep learning Model for Classificati...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Binju Saju V Asha Neethu Tressa Pooja B Kondikoppa Pooja Pavan Kumar V J Department of Computer Science and Engineering (Artificial Intelligence) Adi Shankara Institute of Engineering and Technology Kalady India Department of Master of Computer Applications New Horiznon College of Engineering Bengaluru India

Men are much less likely than women to develop breast cancer. Breast lumps, bloody nipple discharge, and changes in the nipple's or breast's shape or texture are all indications of breast cancer. The study proposes a hybrid model to classify Diabetic retinopathy images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained 96% accuracy, 98.1% sensitivity, 98.2% specificity, 98% precision, and 98.18% F1 Score.

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

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Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning 3

Predictive Design for Quality Assessment Employing Cloud Com...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Jindal, Gaurav Tiwari, Vidhika Mahomad, Riyaz Gehlot, Anita Jindal, Mimoh Bordoloi, Dibyhash G L Bajaj College of Technology and Management Department of Master of Computer Applications Gr. Noida India DPGITM Mechanical Engineering Department Gurugram India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India National Informatics Centre Delhi India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure. © 2023 IEEE.

关键词： Cloud computing

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