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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： N. Naveen Kumar Prabhakaran Abraham Syed Ibrahim Kaliba Diniesh VC Maruthamuthu R Balamurugan Seshu Kumar P Naveen Vekkudu Department of Computer Applications Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering Mai-nefhi College of Engineering and Technology Mai-nefhi Eritrea Department of Electronics & Communication Engineering Kongu Engineering College Erode Tamil Nadu India Department of Information Science and Engineering Jain University Bangalore Karnataka India

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively.

关键词： Representation learning Deep learning Sentiment analysis Toxicology Social networking (online) Computational modeling Transformers Hardware Surges Long short term memory

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A Comparative Study of YOLOv5 and YOLOv8 for Helmet Detection Inside ATMs

A Comparative Study of YOLOv5 and YOLOv8 for Helmet Detectio...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Prashant Kumar Sharma Lakshmipriya B Poonguzhali N Jayalakshmy S School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Engineering Jawaharlal Nehru University New Delhi India School of Computer & System Sciences Jawaharlal Nehru University New Delhi India Electronics & Communication Engineering IFET College of Engineering Villupuram India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Automated helmet detection inside automated teller machine (ATM) cabin for cash dispense can enhance security as well as serves as a means to prevent heist and vandalism. This study presents a comparative study on helmet detection inside ATMs using two prominent object detection you look only once (YOLO) v5 and YOLOv8. The system facilitates detection of helmets from closed circuit television (CCTV) footages to help in identifying the people entering into the ATM by wearing helmets and also in avoiding potential intrusion. In addition to precision and recall, mean average precision (mAP), a standard object detection metric to measure the accuracy of localization is used in this paper to assess the performance of helmet detection models constructed using YOLOv5 and YOLOv8. Experimental evaluation carried out in this study elucidates that YOLOv8 performs better than YOLOv5. By integrating the proposed model with IoT techniques, this model improves the safety and security. In addition, the incorporation of other alert models with this system offers a safer environment for the users inside the ATM cabin.

关键词： YOLO Training Performance evaluation Location awareness Head TV Automated teller machine Safety Security Standards

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Geo-social: Location and social metrics enabled MoNs routing algorithms 12

Geo-social: Location and social metrics enabled MoNs routing...

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12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021

作者： Bharamagoudar, Geeta R. Malligawad, Shrikant Shobha, R.B. Madagi, Manohar Totad, Shashikumar G. Manjula, R.B. Madhanhavi, Lalitha Bharamagoudar, Subhas R. Department of Computer Science & Engineering KLE Institute of Technology Hubballi India Computer Science & Engineering KLE Institute of Technology Hubballi India Department of Computer Science & Engineering KLE Technological University Hubballi India Department of Electronics & Communication Basaveshwar Engineering College Bagalkot India School of Electronics & Communication Reva University Bangalore India Department of Electronics & Communication S. R. Polytechnic Guledgudd India

Mobile opportunistic networks (MONs) are intermittently connected networks in which a multitude of mobile devices are carried by people and packets are delivered among devices via opportunistic communications. In opportunistic networks (OppNets), messages are transmitted between nodes in an opportunistic fashion following a store, carry-and-forward approach and a routing protocol, with the hope to reach their destinations. Routing in MONs is very challenging as it must handle network partitioning, long delays, and dynamic topology. In existing routing protocols for OppNets, different parameters such as node’s delivery predictability, node’s mobility, distance with respect to destination, node’s energy consumption, contact duration between nodes etc. are considered in the selection of the best suitable next hop to carry the message toward the destination. In this paper we have presented a routing protocol which considers the location history, access patterns of mobile user as its feature and infer the location and social relationship between the mobile users. We have a large dataset of mobile users which is simulated and simulation results show the effectiveness of geo social methods. © Grenze Scientific Society, 2021.

关键词： Delay tolerant networks

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Enhancing Network Function Virtualization and Service Function Chaining: Innovative Optimization Strategies and Their Impact

Enhancing Network Function Virtualization and Service Functi...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change.

关键词： Measurement Technological innovation Costs Machine learning algorithms Heuristic algorithms Machine learning Throughput Network function virtualization Resource management Optimization

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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

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computer, electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes!

关键词： Measurement Training Accuracy Scalability Neural networks Melanoma Predictive models Stability analysis Reliability Thermal stability

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A Review on Wideband High-Gain Low-THz Antennas for Wireless Applications

A Review on Wideband High-Gain Low-THz Antennas for Wireless...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Dhamodharan Srinivasan M Premkumar S Deepa Nivethika P Dhilipkumar S Parameswari M Kalpana Chowdary Department of Computer and Communication Engineering Centre for Blockchain and Cybersecurity Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering SSM Institute of Engineering and Technology Dindigul India School of Computer Science and Engineering VIT University Chennai India Department of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of ECE Sri Sairam Insitute of Technology Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas.

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A Hybrid APF Approach for Obstacle Avoidance in a 3D Environment

A Hybrid APF Approach for Obstacle Avoidance in a 3D Environ...

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Industrial electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Amrita Saini Bhaskar Jyoti Talukdar Rudra Narayan Dash D Chandrasekhar Rao Bikramaditya Das Dept. of Computer Science and Engineering BPUT Rourkela Odisha India Dept. of Electronics and Telecommunication Engineering VSSUT Burla Odisha India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India Dept. of Electronics and Communication Engineering BPUT Rourkela Odisha India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This study's main objective is to look at strategies, for avoiding underwater collisions and planning trajectories while navigating around static obstacles. Traditional Artificial Potential Fields (APF) face challenges such as getting stuck in local minima, which diminishes the path's optimality. Additionally, conventional APF often results in trajectories characterized by sharp turns or sudden changes in direction, particularly in environments populated with numerous obstacles. These obstacles can significantly hinder Autonomous Underwater Vehicles (AUVs) that depend on smooth trajectories for maintaining stability and control. To address these drawbacks, we present the Hybrid A* - Artificial Potential Field (HA-APF) algorithm. When using AUVs for collision avoidance duties, adherence to safety procedures is crucial. We propose an enhanced HA-APF algorithm to address this issue for efficient path planning and collision avoidance. Additionally, the A* algorithm, which has been altered to mitigate the innate problems with local optimality in the conventional APF approach, is used in conjunction with the improved APF technique. Finally, validations are conducted in three different regions with different beginning and finishing positions. These tests show how feasible and successful the suggested algorithm is in assisting AUVs in avoiding collisions while also carrying out path-planning duties. Through this work, we provide a robust tool for advancing AUV capabilities in real-world applications, ultimately allowing for more effective underwater exploration and intervention.

关键词： Three-dimensional displays Navigation Stability analysis Hybrid power systems Trajectory Planning Safety Collision avoidance Faces Finishing

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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Ramesh Singh Rawat Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems.

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A Hybrid Learning-Based Q-Commerce Framework Evolving from E-Commerce

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： X. Arogya Presskila J. Relin Francis Raj P. Selva Kumar G. Ram Sankar G. Vinoth Rajkumar P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Management Studies Francis Xavier Engineering College Tirunelveli India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid Learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised Learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain Integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce.

关键词： Costs Scalability Demand forecasting Edge AI Real-time systems Fraud Electronic commerce Security Optimization Logistics

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions

Performance Evaluation of Triple-Branch Diversity Receivers ...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper provides an in-depth analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems.

关键词： Wireless communication Fading channels Degradation Technological innovation Adaptive systems Rician channels Modulation Receivers Encoding Signal to noise ratio

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