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检索条件"机构=Computer Science and Computer Engineering"

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Building Empathic Chabot using LSTM and NLP 4

Building Empathic Chabot using LSTM and NLP

引用

4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Jeyalakshmi, Jeyalakshmi Srinidhi, S. Balaprathap, C. Department of Computer Science and Engineering Amrita VishwaVidhyapeetham Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

This research work presents a Empathic chatbot leveraging advanced natural language processing techniques. The chatbot employs a deep learning architecture, specifically a variant of the Transformer model, to generate human-like responses in real- time. The training dataset includes diverse conversational patterns to enhance the bot's ability to handle a wide range of user inputs. The model is fine-tuned through an iterative process, optimizing for coherence, relevance, and contextual understanding. Evaluation metrics demonstrate the effectiveness of the generative chatbot in providing contextually appropriate and coherent responses across various domains. The research contributes to the evolving field of conversational AI, showcasing the potential of generative models for creating engaging and context-aware chatbot interactions. © 2024 IEEE.

关键词： Natural language processing systems

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Elective Course Recommendation System Using SVD Algorithm

Elective Course Recommendation System Using SVD Algorithm

引用

Congress on Smart Computing Technologies, CSCT 2023

作者： Rahman, Md. Mijanur Oaishi, Chelsea Rifat Mahmud, Jubaer Sadat, Md. Nazmus Department of Computer Science and Engineering Southeast University Dhaka Tejgaon Bangladesh

ISBN: (纸本)9789819780952

The recommender system is studied widely and applied in different domains in this era. However, the domain of course recommendation, particularly for elective courses, has yet to be thoroughly explored. Within the realm of student decision-making, the task of discerning the most fitting elective courses stands as a formidable challenge;however, in the context of this research, we tackle this predicament through the introduction of a collaborative filtering recommendation approach, which harnesses the information encapsulated within students’ prerequisite courses and the mean GPA of those prerequisites to furnish elective course recommendations predicated on the resemblances observed among prior students. The methodology employs the Singular Value Decomposition (SVD) algorithm to quantify the similarity between students and the elective courses’ recommendation procedure. The envisaged framework aims to reorient itself towards furnishing systematically precise and reasonable suggestions to students pursuing elective courses. We utilize real-world datasets to evaluate the proposed system and assess the model's performance using two popular evaluation matrices: root mean square error (RMSE) and mean absolute error (MAE). The proposed machine learning model experimental results demonstrate that the collaborative filtering recommendation system performs well. It offers students a precise forecast of the most appropriate Top-N elective courses, empowering them to discern a specific trajectory for their academic journey, facilitating informed decisions when selecting elective courses, and concurrently serving as a strategic means to attain a commendable outcome in their university pursuits. Furthermore, the proposed model contributes to the broader field of recommender systems by extending their application to the domain of course recommendation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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Revolutionizing Healthcare: The Role of AI and BigData Analytics in Disease Prevention and Management 4

Revolutionizing Healthcare: The Role of AI and BigData Analy...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gatkine, Vaishnavi Khode, Saksham N. Gourshettiwar, Palash Computer Science & Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha440001 India

ISBN: (纸本)9798331523923

Artificial Intelligence (AI) and Big Data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI, by employing machine learning and deep learning, processes big and complex datasets, whereas Big Data detects patterns and predicts risks, enhancing resource utilization and readiness for health emergencies. Yet, the technologies are faced with a range of challenges that include data security concerns, matters of integrating many systems, and the requirement of real-time processing in ever-changing healthcare environments. Although recent technological advancements such as sophisticated deep learning algorithms and decentralized data processing have been promising solutions to some of these challenges, issues of easy integration and privacy remain. This paper suggests an objective-oriented framework to enhance the application of AI and Big Data solutions in healthcare to overcome current limitations and establish a more efficient, personalized, and predictive healthcare system. © 2025 IEEE.

关键词： Adversarial machine learning

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Adaptive Navigation and Surveillance System: A Comprehensive Real-Time Solution for Mobility Assistance and Environmental Monitoring

Adaptive Navigation and Surveillance System: A Comprehensive...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Dhaarini, G. Sanjai, R. Sandosh, S. School of Computer Science and Engineering Vellore Institute Of Technology Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The rapidly changing urban landscape introduces unprecedented challenges to mobility and security, especially for the visually impaired and surveillance operators. All existing assistive technologies and monitoring systems significantly lack real-time environmental awareness, detection of obstacles, and comprehensive threats. Current approaches, mainly based on traditional mobility aids and conventional surveillance mechanisms, fail to provide adaptive and intuitive solutions that address the complex needs of navigation and safety in a modern urban environment. The Adaptive Navigation and Surveillance System is thus the innovative technological intervention that seeks to revolutionize environmental monitoring with high-performance ultrasonic sensing, multi-modal feedback mechanisms, and intelligent processing. The servo-mounted ultrasonic sensor features 180-degree scanning capability while also using sophisticated Arduino-based processing as well as a color-coded radar visualization interface, allowing it to take a transformative approach to personal mobility assistance and security infrastructure. The research will develop flexible, context-aware technological frameworks in support of enhancing situational awareness, real-time threat detection, and a more inclusive safer urban experience by bridging those critical gaps in existing assistive and surveillance technologies. Therefore, the system represents a leap forward in addressing the problems of navigation and security in increasingly intricate urban landscapes. © 2025 IEEE.

关键词： Environmental monitoring

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Finding and Grasping: The Last-Mile of Object Goal Navigation 41st

Finding and Grasping: The Last-Mile of Object Goal Navigat...

引用

41st computer Graphics International Conference, CGI 2024

作者： Cui, Yongzheng Chen, Bolei Yang, Haonan Zhong, Ping Sheng, Yu School of Computer Science and Engineering Central South University Hunan China

ISBN: (纸本)9783031820236

Interacting with object instances is crucial in specific embodied tasks such as rescue and industrial services, not just navigating to the target. Previous research on Object Goal Navigation (ObjNav) has only addressed navigating to the object’s vicinity. However, the interface between approaching the object and finely localizing and grasping it has yet to be adequately investigated. This paper presents the Finding and Grasping (FaG) framework, which is based on the mobile manipulator, to address the last-mile ObjNav problem. The framework incorporates an object localization module and a sub-target extraction module into a traditional autonomous exploration and navigation pipeline. The object localization module uses a grasp estimation method based on point cloud completion to determine the global goal. In another module, a sub-target extraction algorithm is used to break down global goals into navigation and grasping sub-goals. The proposed framework effectively solves the ObjNav last-mile problem and is superior to the baseline in all settings, as demonstrated by sufficient experiments. Furthermore, real robot grasping experiments were conducted to verify the method’s feasibility. A demo video is available at: https://***/AGlIO-DBM1g © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Industrial research

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Greener and Energy-Efficient Data Center for Blockchain-based Cryptocurrency Mining 4

Greener and Energy-Efficient Data Center for Blockchain-base...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Mahmud, Asif Safin Kamal, K.M. Reza, Ahmed Wasif Department of Computer Science and Engineering East West University Dhaka Bangladesh

Cryptocurrency mining data centers consume 100-200 times more energy than conventional office areas annually. Regulating power consumption, cooling mechanisms, and thermal control performance is crucial to creating a greener and more energy-efficient crypto-mining data center. This paper presents a new cryptocurrency mining data center design that is both environmentally friendly and energy-efficient. The design considers popular green and energy-saving data center cooling and temperature management approaches, as well as cost-effective operations. The total monthly cost of the proposed data center is 358025 USD, with renewable energy generating 68520 kW of electricity. The monthly profit from Bitcoin mining is 3200806.969 USD, while Ethereum mining is 2317353.503 USD. The PUE number is 1.04, and the DCiE is 96.15 percent. These statistics help determine the model's conclusion. © 2025 The Authors. Published by Elsevier B.V.

关键词： Bitcoin Blockchain-based Cryptocurrency Data center Ethereum Greener

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A Smart Approach for Early Detection of DDoS Attacks: Artificial Neural Network and Random Forest Hybridization 4

A Smart Approach for Early Detection of DDoS Attacks: Artifi...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Ongshu, Ishmam Ahmed Reza, Ahmed Wasif Uddin Aksir, Md. Emad Alam, Mohammed Tasiful Haq, Md. Mahfuzul Alam, Farhana Department of Computer Science and Engineering East West University Dhaka Bangladesh

Advances in networking technology have made Distributed Denial of Service (DDoS) attacks a real danger to today's networks. Using logical reasoning, the network flow circumstances may be classified as an attack or a routine state to mimic DDoS detection. This research builds an Artificial Intelligence (AI) system using current improvements in Detection System (DS) and Artificial Neural Network (ANN) algorithms advances. It examines User Datagram Protocol (UDP) foods, ping foods, Transmission Control Protocol (TCP) foods, and land attacks to better understand attack behavior. The categorization model for DDoS attacks is constructed using machine learning approaches. Once trained and evaluated, the model can identify unlabeled benign or malicious network data. Experiments reveal that Decision Tree (DT), Random Forest (RF), Naive Bayes, and ANN are more accurate in separating ordinary and attack traffic. The ANN is used to extract optimal features from Internet of Things (IoT) Intrusion Detection System (IDS) data. The DS Algorithm, a new RF optimizer, is employed for effective feature selection. Performance evaluation of the resulting model called Artificial Neural Network-Random Forest (ANN-RF), is done using the "Application DDoS Layer Dataset". RF was selected because it trains faster than DT. We have got 99.998% better accuracy than 99.930% which is the most efficient accuracy from previous work in this field. As per our results, the proposed work has detected smart accuracy and can detect it in real time while keeping the network connected at the same time. Furthermore, we do thorough empirical comparisons of various optimization techniques utilizing a variety of categorization performance metrics. The results confirmed that the proposed technique had a competitive performance across all datasets. © 2025 The Authors.

关键词： Random forests

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Exploiting Open Source Tools for FPGA Design Flow 16

Exploiting Open Source Tools for FPGA Design Flow

引用

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

作者： Tella, Rajashekhar Reddy Agarwal, Shubh Kondepu, Koteswararao Iit Dharwad Department of Computer Science and Engineering Dharwad India

ISBN: (纸本)9798350383119

The escalating demands of data processing applications have propelled Field Programmable Gate Array (FPGA)s into the forefront of programmable accelerators in modern computing systems. Design automation or computer-aided design (CAD) for FPGAs has played a critical role in the rapid advancement and adoption of FPGA technology over the past two decades. Traditionally, these design flows are closed-source and highly specialized to a particular vendor's devices. This paper demonstrates a fully free, open-source software (FOSS) for the FPGA design flow that works for multiple boards. Here, all the open-source tools are installed and packaged as a container and integrated with a federated platform for easy access to the users. The results show that the end-to-end resource allocation for reserving the FOSS container is around 11sec. © 2024 IEEE.

关键词： Containers Docker FPGA Jupyter Notebooks Nextpnr Open-source Yosys

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A Fast Spectral Clustering Algorithm Based on Granular-Ball and Similarity Graph Approximation 3

A Fast Spectral Clustering Algorithm Based on Granular-Ball ...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Ye, Xueqi Chen, Linshu Hu, Anxing Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331506582

Spectral clustering is widely used due to its effectiveness in handling nonlinear data, but its performance is limited by high computational complexity when processing large-scale datasets. To address this issue, this paper proposes a fast spectral clustering algorithm based on granular-balls and similarity graph approximation (GBASC). Through granular-ball computation, fine-grained data is transformed into coarse-grained granular-balls, reducing the scale of the similarity graph. The paper proposes a fast approximation algorithm for granular-ball similarity graphs based on kernel density estimation, achieving a sparse approximation of the fully connected granular-ball similarity graph. This method significantly reduces the time and space complexity of constructing the granular-ball similarity graph, and by generating sparse matrices, it improves the efficiency of Laplacian matrix eigenvalue decomposition. Experimental results demonstrate that GBASC achieves high clustering performance and computational efficiency on large-scale datasets. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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Traffic Sign Recognition using Shift In-variant 2-D ConvNet 7

Traffic Sign Recognition using Shift In-variant 2-D ConvNet

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramsundar, B. Sudharsan, S. Pravin, A. Sathyabama Institute of Science And Technology Dept of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

This research introduces a novel method for traffic sign identification utilizing a variant 2-D convolutional neural network (CNN) architecture with a shift-invariant operation. Addressing challenges such as variations in size, illumination, occlusions, and perspective, the proposed approach efficiently learns and identifies traffic signs regardless of their location in the input image. The CNN architecture employs common convolutional layers followed by separate shift-invariant convolutional layers for each type of traffic sign, enabling the network to extract both shared and individual traits effectively. Tests conducted on benchmark datasets including the LISA Traffic Sign Dataset (LISA-TSD) and German Traffic Sign Recognition Benchmark (GTSRB) demonstrate superior recognition accuracy compared to state-of-the-art methodologies. The hierarchical design of the proposed model enhances recognisability, making it suitable for intelligent transportation systems. Overall, the study presents a promising variant 2-D CNN model for traffic sign identification with significant potential for real-world applications. © 2024 IEEE.

关键词： Traffic signs

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