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Machine Learning Techniques for Identifying Child Abusive Texts in Online Platforms

Machine Learning Techniques for Identifying Child Abusive Te...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Mohammad Kamal Uddin Mohammad Tarek Aziz Mohammad Shahadat Hossain Karl Andersson Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study addresses the critical issue of child abuse in digital communications by developing advanced machine learning and deep learning models for detecting child abusive texts in the Bengali language on online platforms. The primary goal of this research is to contribute to child abuse prevention by creating an effective tool for accurately identifying abusive content. Utilizing a combination of natural language processing (NLP) techniques and deep learning algorithms, this model distinguishes between abusive and non-abusive text. The dataset, comprising manually collected and labeled comments and posts from social media sites, is categorized into Positive, Neutral, and Negative classes. For model development, a variety of machine learning algorithms including RF, DT, and SVM, alongside deep learning approaches such as LSTM and CNN, were tested. The performance of the proposed model was assessed using precision, recall, and F1 score metrics. The findings reveal that the model performs with high accuracy in classifying abusive text in Bengali, offering significant potential as a tool for effective child abuse identification and prevention.

关键词： Deep learning Support vector machines Radio frequency Measurement Machine learning algorithms Social networking (online) Prevention and mitigation Computational modeling Natural language processing Long short term memory

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash 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 School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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Deep Neural Networks in Neuroimaging: A U-Net Model for Precise Brain Tumor Detection

Deep Neural Networks in Neuroimaging: A U-Net Model for Prec...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331541583

This work holds the promise of being a considerable breakthrough in medical imaging diagnosis by studying advanced U-Net topologies for accurate brain tumour segmentation from magnetic resonance imaging (MRI) data. In this research, we present a more sophisticated pipeline that covers data preprocessing, robust model validation, and architecture tuning of the deep learning techniques. Performing accurately at 94.7% accuracy, the model showed a decent precision and recall rates with extremely promising results against traditional segmentation approaches as well as current deep learning frameworks. These results show how U-Net models could improve the diagnoses accuracy and guide personalized treatment tactics. Our research demonstrates the transformative ability of deep learning in medical diagnostics and opens a pathway for additional strides in individualized therapy, albeit diffuse tumour border segmentation barrier and MRI image heterogeneity limitations. © 2024 IEEE.

关键词： Neuroimaging

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Demystifying the Applications of Internet of Medical Things (IoMT) for smart health care system

Demystifying the Applications of Internet of Medical Things ...

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Data, Information and Computing science (CDICS), International Conference on

作者： Ruqqaiya Begum Polisetty Swetha Koti Tejasvi M. A. Jabbar Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350382778

ISBN: (纸本)9798350382785

As Internet of Things (IoT) devices become more commonplace and are used in more aspects of daily life, a bright future for people, data, and processes is emerging. Because of its potential to enhance patient care as well as its capacity to deliver more trustworthy clinical data, boost productivity, and save costs, In recent years, the Internet of Medical Things (IoMT) has a strategic goal for future e-healthcare. It is understandable why so many healthcare organization’s nowadays want to take advantage of what the IoMT has to offer. Its infrastructure includes interconnected medical equipment, software, and care systems and services. For everyone to understand how IoMT is enhancing the health care system with the aid of other technologies, we have described the implementation of IoMT in detail in this paper. Along with this, we have also discussed the different security concerns and difficulties the healthcare system is experiencing due to the IoMT’s information-gathering shortcomings.

关键词： Productivity Biomedical equipment Costs Smart healthcare Internet of Medical Things Software Security

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Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring

Improved Vehicle Sub-type Classification for Acoustic Traffi...

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2023 National Conference on Communications, NCC 2023

作者： Ashhad, Mohd Goenka, Umang Jagetia, Aaryan Akhtari, Parwin Ambat, Sooraj K. Samuel, Mary Jamia Hamdard Dept. of Computer Science & Engineering New Delhi India IIIT Lucknow Dept. of Information Technology Lucknow India Nit Rourkeela Dept. of Mechanical Engineering Roorkela India Defence R&d Organization Naval Physical & Oceanographic Laboratory Kochi India IIIT Lucknow Dept. of Mathematics Lucknow India

ISBN: (纸本)9781665456258

The detection and classification of vehicles on the road is a crucial task for traffic monitoring. Usually, computer Vision (CV) algorithms dominate the task of vehicle classification on the road, but CV methodologies might suffer in poor lighting conditions and require greater amounts of computational power. Additionally, there is a privacy concern with installing cameras in sensitive and secure areas. In contrast, acoustic traffic monitoring is cost-effective, and can provide greater accuracy, particularly in low lighting conditions and in places where cameras cannot be installed. In this paper, we consider the task of acoustic vehicle sub-type classification, where we classify acoustic signals into 4 classes: car, truck, bike, and no vehicle. We experimented with Mel spectrograms, MFCC and GFCC as features and performed data pre-processing to train a simple, well optimized CNN that performs well at the task. When used with MFCC as features and careful data pre-processing, our proposed methodology improves upon the established state-of-the-art baseline on the IDMT Traffic dataset with an accuracy of 98.95%. © 2023 IEEE.

关键词： Machine learning

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks 2

Enhancing Stock Price Predictions Through LSTM-based Recurre...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishna Kumar, G.V. Natarajan, B. Venkatraman, K. Devi, S. Gayathri Selvam, Prabu Nagarajan, N.R. Amrita Vishwa Vidyapeetham Dept. of Artificial Intelligence Chennai India VIT University School of Computer Science and Engg. Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of CSE Chennai India Sri Ramakrishna Institute of Technology Dept. of Computer Science and Engg. Coimbatore India K. Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350317558

Analyzing finances has become increasingly challenging in today's investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors' profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in the stock index or forecasting prices for individual companies based on their daily concluding rates. The proposed methodology introduces a model-agnostic method. Rather than conforming data to a specific model, this approach aims to identify unseen trends inherent in the data through deep learning models. In this study, three distinct deep learning models - HMM, RNN, and LSTM - are employed to predict prices using the dataset from ICICI Bank. Their performances are compared, revealing that the LSTM model adept.y discerns evolving trends. The LSTM model displays the lowest error percentage at 2.36%, outperforming other models such as HMM (7.32%) and RNN (3.94%). © 2024 IEEE.

关键词： deep learning LSTM stock market stock Price prediction Time series

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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VOID - An Assistant (Chatbot) to Education Queries System

VOID - An Assistant (Chatbot) to Education Queries System

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Chakraborty, Debaissh Mishra, Anjana Kumar, Vikash Singh, Saloni Hani, Harshita C.V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665461092

Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot afford man-power to interact with clients and customers 24×7 so that chatbot has come into play which does the same work with no extra cost. It is a new and emerging technology that is in high demand across a wide range of businesses. It is used to simulate a human like conversation and a machine to relieve the workload. It can be easily deployed in any field where products must be developed and evaluated before being released. During the admission time in college, reception is packed, and people have to wait for their turn, and this entire process takes too much time. Although every college has their own official website, but everyone cannot find the appropriate answer of their query. The objective of this paper is to reduce the workload by developing an interactive environment between a human and a machine that gives a reply to the query of the user on behalf of a human for the education system. © 2022 IEEE.

关键词： Query processing

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and Grid Computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 IEEE.

关键词： Smart contract

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