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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Muneeshwari, P. Suguna, R. Valantina, G. Mary Sasikala, M. lakshmi, d. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Trichy India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

Predictive maintenance in industrial settings has seen significant advancements through the integration of Internet of Things (IoT) technologies and data analytics. This study outlines a comprehensive methodology for implementing IoT-driven Predictive Maintenance, focusing on data collection, machine learning model selection, real-time data analysis, and continuous improvement. data Collection and Sensor deployment details the deployment of sensors on machinery, including temperature, vibration, pressure, humidity, and accelerometer sensors. Real-time data collection, strategic sensor placement, and data preprocessing ensure the acquisition of high-quality data for analysis. Machine learning Model Selection highlights the selection of Random Forest and lSTM models for predictive maintenance. These models are trained using historical data, cross-validated, and fine-tuned to optimize accuracy. Real-Time data Analysis and Integration emphasizes the importance of real-time analysis using streaming analytics tools. Alerts are generated when sensor data deviates from predefined thresholds, and the integration with maintenance workflows ensures timely responses. Performance Evaluation and Continuous Improvement presents performance metrics, including precision, recall, and F1-score, demonstrating the effectiveness of the predictive maintenance system. Reduced downtime and decreased maintenance costs showcase its impact. Continuous improvement involves regular updates to models and data preprocessing techniques, with a focus on adapting to emerging technologies. © 2024 IEEE.

关键词： Predictive maintenance

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Advanced Techniques for Early Prediction from CT Scans 4

Advanced Techniques for Early Prediction from CT Scans

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sachdeva, Vrinda Mishra, Nitin Kumar, Arun Singh, Neeru G.L Bajaj Institute of Technology and Management Department of Computer Science Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India G.L Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India

ISBN: (纸本)9798350364729

Visual representation holds significant significance in the healthcare industry. Healthcare professionals previously utilized medical imagery to accurately diagnose diseases and offer food to patients. Healthcare professionals occasionally encountered difficulties in diagnosing operations as a result of intricate medical imaging. Cancer is a noncommunicable disease that is difficult to detect in its early stages. The death rate of human beings rises annually as a result of inaccurate diagnosis. The researchers are encountering a significant obstacle in the analysis of medical imagery connected to cancer. The early detection of cancer is a significant obstacle that must be overcome. Therefore, it is imperative to preprocess medical images in order to detect important aspects. This study applies various image preprocessing algorithms to medical images of lung cancer. An analysis is conducted using a comparison-based approach. © 2024 IEEE.

关键词： lung cancer

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WoC SVC: A Model for Enhancing lullabies Through Personalized Voice Cloning

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Procedia computer Science 2025年 258卷 1565-1575页

作者： Sanika Ardekar Riddhi Sanghani Sahil Nair Ruhina Karani Computer Engineering Department D.J. Sanghvi College of Engineering Mumbai India

This paper introduces the Whisper Of Comfort Singing Voice Conversion (WoC SVC) model, a system designed to transform lullabies sung by any voice into a similar, familiar voice, harnessing the positive impact of familiar voices on infants’ development and well-being. Central to the WoC SVC model is the integration of UnivNet, an advanced neural vocoder architecture, which facilitates the generation of high-quality audio waveforms while preserving the original content and melody. The system architecture comprises essential components, including a Content Encoder for extracting mel-spectrograms and log loudness features, a Soft Content Encoder (ContentVec) for generating compact content representations, and a Speaker Embedder for capturing target voice characteristics. These representations are concatenated and processed through UnivNet to synthesize audio waveforms in a similar, familiar voice. Comparison between SOVITS SVC model and WoC SVC is made and the result is evaluated using the values from MFCC cosine similarity and PESQ scores where WoC SVC has consistently performed better. The PESQ score for WoC SVC is 1.4929 and for SOVITS SVC is 1.1348, indicating a percentage difference of approximately 31.53%. Similarly, the cosine similarity for WoC SVC is 0.9346124 and for SOVITS SVC is 0.8947109, with a percentage difference of around 4.46%. These results demonstrate that WoC SVC outperforms SOVITS SVC.

关键词： Singing Voice Conversion (SVC) lullabies UnivNet Mel-spectrograms Encoder Vocoder Perceptual Evaluation of Speech Quality (PESQ) Cosine Similarity

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Effective Segmentation of Brain Tumors through the GOA Algorithm using deep learning 2

Effective Segmentation of Brain Tumors through the GOA Algor...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Haritha, V. Babu, J.Jagan Saranya, R. Yogaraja, C.A. Rajalakshmi, S. Manimegalai, l. R.M.K Engineering College Computer Science and Engineering India R.M.D Engineering College Kavaraipettai India Rmk College of Engineering and Technology India Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India R.M.K. College of Engineering and Technology Electronics and Communication Engineering India

ISBN: (纸本)9781665456302

Image segmentation is used to isolate aberrant tumor regions inside the brain, which is a necessary step in the diagnostic procedure. The tissue of the brain is required for the Magnetic Resonant Image (MRI) for segmenting the brain tumor. The primary objective of this proposed work is to develop a non-invasive approach to estimate the grade of a Brain Tumor (BT) using MRI image. Tumor area was recovered from postprocessed image employing the fuzzy C means methodology. The texture, local binary configuration (lBP) and fractal characteristics were recovered when the characteristics were extracted. The settings for three alternative classification algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), were then accustomed using the Grasshopper Optimization Algorithm (GOA). Finally, the performance of three graders was assessed before and after optimizing. © 2023 IEEE.

关键词： Support vector machines

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node detection and Secure Routing framework (CMNdSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15381-15395页

作者： Ni, Xuefeng Fieguth, Paul W. Ma, Ziji Shi, Bo liu, Hongli Hunan University College of Electrical and Information Engineering Changsha410012 China University of Waterloo Department of Systems Design Engineering WaterlooONN2L 3G1 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Sai Kung Hong Kong

Anomaly detection for rail running bands, the pattern of wheel-rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). © 2004-2012 IEEE.

关键词： Network theory (graphs)

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Sign language Translation and Voice Impairment Support System using deep learning 5

Sign Language Translation and Voice Impairment Support Syste...

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5th International Conference on data Intelligence and Cognitive Informatics, ICdICI 2024

作者： Shajeena, J. Shiny, R.M. Mary Vespa, M. Kavitha, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

The Sign language Translation and Voice Impairment Support System (SlT-VISS) represents a groundbreaking application of deep learning methodology aimed at facilitating communication for individuals with hearing impairments or speech disabilities. By utilizing Convolutional Neural Network (CNN), OpenCV for image processing, and Pyttsx3 for text-to-voice conversion, SlT-VISS offers real-time translation of sign language gestures into both text and speech output. Using a camera as input, the CNN algorithm accurately identifies and classifies sign language gestures displayed in front of it. OpenCV complements this process by preprocessing the input images for gesture segmentation and feature extraction, enhancing the accuracy of gesture recognition. Once a gesture is recognized, Pyttsx3 converts the translated text into synthesized speech, providing immediate auditory feedback to the user. SlT-VISS is designed to bridge the communication gap faced by individuals with communication disabilities, enabling them to express themselves effectively in various social and professional settings. By seamlessly translating sign language into both text and speech, this system promotes inclusivity and accessibility, empowering users to communicate with confidence and independence. The integration of CNN, OpenCV, and Pyttsx3 technologies within SlT-VISS signifies a significant advancement in assistive technology. As technology continues to evolve, further enhancements to SlT-VISS holds the potential to revolutionize communication accessibility and enable greater inclusivity in society. © 2024 IEEE.

关键词： Convolutional neural networks

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Travel Assist: Resolution of Travel Queries using Advanced Natural language Processing for India 1

Travel Assist: Resolution of Travel Queries using Advanced N...

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1st IEEE International Conference on Communications and computer Science, InCCCS 2024

作者： dheemonth, K. Ujwal, P. Fernandes, Rollin John Acharya, Madhvesh M. Shettar, Rajashree Badiger, Sujata d. Tiwari, Sourabh Rashmi, T.S. Khaitan, Prashant Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India Voice Intelligence Group Samsung R&D Institute Bengaluru India

ISBN: (纸本)9798350358858

In daily life, users travel using various modes such as plane, train, buses, ships and cabs. With advancement of technology, web and application interface provides features to search routes, seat availability, reservation and live location of flight and trains etc. For India market, individual solutions for segments such as flights, trains, cabs, buses etc. are available. A common solution with all the features using latest artificial intelligence techniques is proposed using this paper. Voice assistants and voice-based queries are getting popular and is easy to use while travel. The research paper talks about aspects to consider a common platform Travel Assist for resolution of all kind of travel queries using advanced Natural language Processing (NlP). Intents and slots are defined based on market survey, and then training data is generated. Random Forest based model is trained to classify any user utterance into intent and slots. Required web APIs are used to get the real time results. To make it easily available to large audience, paper explains the uses of the Samsung Bixby Capsule framework. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing Road Safety and User Experience through Intelligent Tollgate Systems and Vehicular Communication Networks 5

Enhancing Road Safety and User Experience through Intelligen...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Jebaraj, Bharath Singh Nasreen, M.Ayeesha Sushmitha, S. Azarudeen, A. Subha, T.d. Vivekrabinson, V. Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India R.M.D. Engineering College Department of Electronics and Communication Engineering Thiruvallur India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Thiruvallur Department of Electronics and Communication Engineering Thiruvallur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350367171

The automotive industry's growth, fueled by technology and job opportunities, has heightened global automobile sales. despite this progress, road safety remains a concern, with numerous lives lost in car accidents annually. To address this, Vehicle-to-Vehicle (V2V) communication emerges, enhancing road safety through real-time information exchange. Roadside Communication Units (RCUs) provide vital services, offering traffic updates, weather notifications, and immediate alerts about incidents. Intelligent tollgate systems in vehicles streamline transactions, and speed control systems ensure safer driving. leveraging Zigbee technology enables seamless communication with toll plazas, improving traffic flow. This study aims to enhance road safety through intelligent tollgate systems, V2V communication, and innovative technologies, emphasizing their potential to optimize traffic flow and provide a more comfortable driving experience. Identified are future research directions and development opportunities in this dynamic field. © 2024 IEEE.

关键词： Zigbee

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network data

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SN computer Science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The degree of Correlation Network Model (dCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based deep Analyzing Network (SA-dAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification degree of correlation network model (dCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-dAN) Semantic analysis Social data

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