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共 17471 条记录，以下是131-140 订阅

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing 12

Parallel Neural Computing for Scene Understanding from LiDAR...

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12th International Conference on Intelligent Systems and Embedded Design, ISED 2024

作者： Sah, Suwesh Prasad Chinara, Suchismita National Institute of Technology Dept. Computer Science and Engineering Rourkela India

ISBN: (纸本)9798331522612

Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time. This paper proposes a novel baseline architecture for developing sophisticated models capable of true hardware-enabled parallelism, achieving neural processing speeds that mirror the agent's high velocity. The proposed model (Parallel Perception Network (PPN)) consists of two independent neural networks, segmentation and reconstruction networks, running parallelly on separate accelerated hardware. The model takes raw 3D point cloud data from the LiDAR sensor as input and converts it into a 2D Bird's Eye View Map on both devices. Each network independently extracts its input features along space and time dimensions and produces outputs parallelly. The proposed method's model is trained on a system with two NVIDIA T4 GPUs, using a combination of loss functions, including edge preservation, and demonstrates a 2x speedup in model inference time compared to a sequential configuration. Implementation is available at: github/ParallelPerceptionNetwork. Learned parameters of the trained networks are provided at: huggingface/ParallelPercentionNetwork. © 2024 IEEE.

关键词： Deep learning

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Fruit Identification using YOLO v8 and Faster R-CNN -A Commparative Study 2

Fruit Identification using YOLO v8 and Faster R-CNN -A Commp...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sawant, Kedar Shirwaikar, Rudresh D. Ugavekar, Raj Ravindra Chodankar, Chandrakant Shyam Tirodkar, Bhupesh Nilkant Padolkar, Ashwek Gawas, Pramesh Agnel Institute of Technology and Design Dept. Computer Engineering Mapusa India

ISBN: (纸本)9798350382952

Fruits are rich in nutrients, vitamins, and minerals and play a crucial role in both dietary health and a nation's economy. In India, diverse fruit cultivation necessitates efficient identification methods. The manual fruit identification processes are time-consuming and biased, leading to agricultural inefficiencies. Hence, the adoption of machine learning and deep learning has gained immense popularity in the automation of the fruit identification preprocess. This study evaluates the performance of DL models, Faster R-CNN and YOLO v8, on a dataset featuring five fruits namely Apple, Cashew Apple, Banana, Mango and Orange for identifying fruits with the comparison between them. While both models yield impressive results, Faster R-CNN generally exhibits higher mAP. However, YOLO v8 excels in precision for specific fruits like Cashew Apple and Banana. The choice between models involves a trade-off between mAP and precision, emphasizing the need for further optimization considering factors like dataset variations, computational performance, and real-world applicability. © 2024 IEEE.

关键词： Fruits

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Evolve Aggregation Management of Data for Heterogeneity 3

Evolve Aggregation Management of Data for Heterogeneity

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kolekar, Vikas Patil, Medini Shete, Shreyash Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

Cloud computing is the most emerging technology. It has the capability to provide various services like infrastructure, security, data management, databases, and network over the internet. Several cloud service providers in the market provide computing resources. Cloud brokering in cloud computing is a crucial part as it acts as a middleware between cloud users and cloud services providers. In this paper we aim to provide an overview on adaptive data management middleware for cloud brokers. To manage the heterogeneous data and other factors, policy-based task scheduling can be integrated with the cloud broker, which supports dynamic data placement and takes decisions and actions at the runtime considering the SLA (service level agreements) and QoS (quality of service) requirements without any manual involvement. By aggregation of data, the next task of scheduling and assigning priority will be taken carefully. At the end service requests should be fulfilled by utilizing minimum resources. © 2024 IEEE.

关键词： Cloud platforms

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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Route Optimization Using Clustering and K Opt Optimization for Garbage Collection 2

Route Optimization Using Clustering and K Opt Optimization f...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Warrier, Arun Patel, Dilip Ghane, Sunil Vhatkar, Akshay Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350328202

In today's rapidly developing world, emerging technologies are revolutionizing all aspects of life. One important aspect is that of development of Smart *** this concept all domains of a society need to be improved upon. One of the important domains is that of waste management *** from collection of garbage from within the society, to disposing of the waste in an environment friendly and all the steps in between need to carried out in an efficient manner.A major challenge in this process is that of deciding upon a route for garbage collection which can provide the most optimum result in terms of time taken to complete the process, distance travelled by each vehicle, whilst considering different factors such as traffic congestion, capacity of each truck and few others. © 2024 IEEE.

关键词： Traffic congestion

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. Data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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The Stability and Computational Complexity Analysis of Tree-seed Algorithm for Frequency-Constrained 10-bar Truss Problem 5

The Stability and Computational Complexity Analysis of Tree-...

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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Cinar, Ahmet Cevahir Nur Erman, Kadriye Selçuk University Faculty of Technology Dept. of Computer Engineering Konya Turkey Kirikkale University Faculty of Engineering and Natural Sciences Dept. of Computer Engineering Kirikkale Turkey

ISBN: (纸本)9798350352863

Optimization studies in truss problems produce different results according to different optimization conditions, which leads to significant changes in the results obtained. This paper presents a comprehensive analysis of the Tree-Seed Algorithm (TSA) in solving the frequency-constrained 10-bar truss optimization problem, a well-known benchmark in structural engineering characterized by its complex, non-linear, and multi-modal design space. The TSA, inspired by natural processes of tree growth and seed dispersion, is evaluated in the truss problem for its stability, computational efficiency, and ability to find optimal solutions. Through rigorous testing involving 51 independent runs, the TSA consistently achieves optimal solutions with a mean objective function value of 554.4549 and a standard deviation of 1.4049. The algorithm demonstrates rapid convergence, typically within 100 iterations. The design parameters of the frequency-constrained 10-bar truss optimization problem were found by TSA within stability. These findings highlight the TSA's effectiveness and reliability, establishing it as a robust tool for tackling complex engineering optimization problems and suggesting its potential for broader applications in structural optimization. © 2024 IEEE.

关键词： Trusses

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