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5th International Conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Dhobale, Nikita Dinkar Elle, Krisha Ramesh Deshmukh, Pratiksha Vilas Nirne, Sakshi Sidram Jarali, Prof. Ashwini International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350379945

The system entails the development of a sentiment analysis system consisting of two integral modules aimed at analysing audio recordings of calls. The first module predicts sentiment-positive, negative, or neutral-by converting speech to text using speech-to-text APIs and subsequently applying machine learning algorithms including Random Forest, Naive Bayes, SVM, and Logistic Regression. The second module employs audio processing techniques utilising Librosa for feature extraction and Convolutional Neural Networks (CNNs) for emotion recognition. The system facilitates comparisons between the results obtained from both modules for call recordings. Users, starting with system administrators, can choose between two options: browsing for uploading pre-recorded audio files or live recording. In both scenarios, the system executes sentiment analysis and emotion detection, presenting results derived from speech-to-text and audio processing analyses. © 2024 IEEE.

关键词： Emotion Recognition

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Design of improved deer hunting optimization enabled multihop routing protocol for wireless sensor networks

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 363-372页

作者： Balasubramanian, D. Lubin Govindasamy, V. Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

A wireless sensor network (WSN) encompasses a huge set of sensor nodes employed to collect data and transmit it to a base station (BS). Due to its compact, inexpensive, and scalable nature of sensors, WSN finds its applicability in diverse real-time applications. The battery-operated sensor nodes necessitate the design of a multi-hop routing protocol for the effective utilization of available energy in the network. Routing can be considered an optimization problem and can be solved by the design of bioinspired algorithms. This study introduces an improved deer hunting optimization-enabled multihop routing (IDHO-MHR) protocol for WSN. The major intention of the IDHO-MHR approach is to optimally find the routes to the destination in WSN. The IDHO algorithm is initially derived by the incorporation of the Nelder Mead (NM) concept into the traditional DHO algorithm. In addition, the IDHO-MHR technique primarily derives a fitness function with the inclusion of two major variables, namely residual energy (RE) and distance. The nodes with higher RE and minimum distance have the probability of becoming optimal routes from the networks. The performance validation of the IDHO-MHR approach is performed, and the outcomes are inspected in various aspects. The experimental outcomes reported the supremacy of the IDHO-MHR protocol over the other recent approaches. © 2023 The Authors

关键词： Energy efficiency

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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Leveraging a structure-based and learning-based predictor using various feature groups in bioinformatics (case study: protein-peptide region residue-level interaction) 14

Leveraging a structure-based and learning-based predictor us...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Shafiee, Shima Fathi, Abdolhossein Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

ISBN: (纸本)9798331511272

Predicting protein-peptide interactions is crucial for understanding cellular activities, researching protein functions, designing new drugs, studying abnormal cell behavior, and addressing human diseases. Conventional experimental techniques for identifying the residues in protein-peptide interaction regions are labor-intensive and costly. Therefore, determining these binding region residues using computational methods would serve as a valuable and complementary tool. Our research introduces RSPPRI, a computational method that combines a Residual Neural Network and a Support Vector Machine to accurately identify the residues in the binding regions of protein-peptide interactions. To achieve this, we extract various feature groups from protein structures, including evolutionary, physicochemical, and structure-based features. ©2024 IEEE.

关键词： Support vector machines

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Impact of Gate Oxide and Dielectric Pocket on Performance Enhancement of Hetero Gate Oxide TFETs 3rd

Impact of Gate Oxide and Dielectric Pocket on Performance En...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Mahato, Prasenjit Kavi, Kavindra Kumar Swain, Sukanta Kumar Raj, Abhishek Sharma, Shashi Kant Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Department of Electronics and Communication Engineering Motilal Nehru National Institute of Technology Allahabad India Department of Electronics and Communication Engineering Vidya Memorial Institute of Technology Tupudana Ranchi India

ISBN: (纸本)9789819784639

This work reports the impact of hetero gate oxide and dielectric pocket on the performance of conventional TFET. The conventional TFET has been optimized by introducing hetero gate oxide of HfO2-SiO2 and dielectric air-pocket. Both these approaches improve the lateral electric field at the source-channel interface and weakens the lateral electric field at the drain-channel interface. Hence, the ON-current is enhanced and OFF-current is reduced. At last, the optimized value of work-function of the gate metal has been obtained at 4.3 eV. The TCAD simulation results show a significant increase in ON-current (ION = 10.24 × 10−4) and reduction in leakage current (IOFF = 9.659 × 10−17). The value of ION/IOFF ratio and subthreshold swing was found to be 1.06 × 1013 and 11.776 mV/decade respectively that confirms a considerable improvement in the performance of the proposed TFET structure. The transconductance for the proposed device structure was found to be 1.935mS. These improvements confirm that the proposed device DP-HGO-DG-TFET is suitable for high frequency and low power applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Leakage currents

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Palladium Electrode-Based Vertical Junctionless FET for High-Performance Hydrogen Gas Sensing 3rd

Palladium Electrode-Based Vertical Junctionless FET for High...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Raj, Abhishek Swain, Sukanta Kumar Kumar, Rishu Sharma, Shashi Kant Department of Electronics and Communication Engineering Indian Institute of Information Technology Jharkhand Ranchi835217 India

ISBN: (纸本)9789819784639

In this research article, a Vertical Junctionless Field Effect Transistor (V-JLFET) is designed for Hydrogen (H2) gas sensing. This device utilizes a palladium (Pd) electrode which is a catalytic metal for sensing of hydrogen gas. When the H2 gas meets catalytic Pd electrode, it creates pressure, causing a shift in the Pd gate workfunction. This change in workfunction is employed to detect the occurrence of H2 gas. Several electrical characteristics such as current through drain (IDS), transconductance (gm), drain conductance (gd), surface potential (Φs), and electron concentration were studied to sense the existence of H2 gas. Additionally, a sensitivity analysis is carried out to assess the effectiveness of the device in terms of threshold voltage (Vth) with varying pressure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitivity analysis

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Vision Aid For Blind People Using YOLOV8 2

Vision Aid For Blind People Using YOLOV8

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： More, Priyanka Sangamkar, Satvik Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367461

This assistive system for the visually impaired integrates real-time YOLOv8 object detection with distance estimation and text-to-speech conversion, empowering users with enriched environmental awareness. Leveraging robust datasets and architectures, it identifies over 90 everyday objects amidst clutter while concurrently approximating object proximity through trigonometric translations of size metrics. Audio cues enunciate recognized items and distances in real time for accessibility. This combination of precise visual recognition, spatial metrics, and assistive feedback holds promise for enhanced mobility, independence, and safety during navigation for visually impaired individuals. © 2024 IEEE.

关键词： Vision aids

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Quantum technology Fueling the Next Generation Optical communication: Challenges and Pathways 5th

Quantum Technology Fueling the Next Generation Optical Commu...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Venkateswara Rao, Ch. Pareek, Prakash Mishra, Jitendra K. Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand834010 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031770777

information transmission through light has attained significant advancements in the fields of both optical fiber communication (OFC) and optical wireless communication (OWC) systems. These advancements have been examined by numerous researchers by employing pioneering modulation schemes, adaptive optics, advanced signal processing algorithms, advancement in materials, and employing sophisticated network architectures for wide variety of applications such as: military;defense;healthcare;satellite;internet of things;and telecommunications. However, the primary factors which affecting the OFC systems are signal attenuation, dispersion, reliability, robustness, and security even though there exists a predominant development. In addition, atmospheric attenuation (due to absorption and scattering), turbulences, climate conditions (fog, rain, snow, dense fog etc,.), alignment and tracking, interference, range constraints, and security are the major challenges in OWC systems in spite of having advanced approaches from past few decades. Hence, these challenges are need to be addressed in order to achieve reliable and high-speed data transmission. This could be possible with the integration of OFC and OWC with emerging quantum communication technologies (quantum key distribution, quantum entanglement, quantum repeaters, quantum sensing, and quantum secured OWC links). Therefore, this work presents the recent advancements in the fields of OFC and OWC systems with the state-of-art-of techniques and approaches. The primary research challenges related to security, speed, range, capacity and adaptability of both OWC and OFC are outlined. In addition, the possible integration of these systems with quantum communication technologies and the recent progression have been outlined. Finally, the possibility of future research direction towards the quantum OFC, quantum OWC have been discussed for ensuring and revealing the full potential of quantum enhanced optical communication systems.

关键词： Adaptive optics

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