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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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Generation of Handwriting Applying RNN with Mixture Density Network

Generation of Handwriting Applying RNN with Mixture Density ...

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International Conference on Automation, Signal Processing, Instrumentation and Control, i-CASIC 2020

作者： Jayashree, D. Pandithurai, O. Shreevathsav, S. Shyamala, P. Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Associate Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India UG students Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (纸本)9789811582202

Handwriting recognition is one of the fields where researchers try to apply different machine learning techniques to identify the words and recreate a meaningful sentences. Recognition has two ways to get an input: online and offline. Online handwriting involves the conversion of text along with continuous input as user writes the text. Offline handwriting involves processing handwriting images without any other data as input. This paper has been worked on using a set of online handwriting data to recognize a variety of features using long short-term memory recurrent neural networks (LSTM RNNs). The trained neural network along with mixture density networks (MDNs) is used to generate offline handwriting samples from text. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Enhancing Relational Database Interaction through Open AI and Stanford Core NLP-Based on Natural Language Interface

Enhancing Relational Database Interaction through Open AI an...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Surjeet Kumar Md Shamsher Alam Zeeshan Khursheed Shadan Bashar Neha Kalam Computer Science & Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains.

关键词： Structured Query Language Databases Natural languages Semantics Relational databases Manuals Reservoirs

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Designing an AI driven intelligent Tutorial System

Designing an AI driven intelligent Tutorial System

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Md Shamsher Alam Surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato Shadan Bashar Anjali Suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy Machine learning algorithms Image recognition Decision making Tutorials Market research

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The Maximum (k, m)-Subsets Problem is in the Class NEXP

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-5页

作者： Challita, Khalil Abdo, Jacques Bou An Associate Professor in the Department of Computer Science Notre Dame University-Louaize Zouk Mosbeh Lebanon An Assistant Professor in the College of Business and Technology University of Nebraska at Kearney United States

In this paper we define and solve the following new problem: Given a set of n elements, we are interested in determining the largest number of subsets of size k that have at most m elements in common. We prove that in the worst-case scenario, a brute force solution requires a double exponential time with respect to n. Afterwards, we show that our problem is in the class NEXP by reducing it to the succinct K-coloring problem. We use in our proof ordered binary decision diagrams to determine whether there is an edge between two nodes of a graph that corresponds to an instance of our problem. We also show that it is at least EXP-hard. © 2021. All Rights Reserved.

关键词： Binary decision diagrams

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Dynamic Defense Model against Eclipse Attacks in Proof-of-Work Blockchain Systems

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Procedia computer science 2024年 235卷 1202-1212页

作者： Surendra Reddy Vinta Sunil A. Patel Aws Zuhair Sameen Mukesh Soni Dr Ihtiram Raza Khan Hayder Mahmood Salman Associate Professor School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Assistant Professor L. D. College of Engineering Ahmedabad College of Medical Techniques Al-Farahidi University Baghdad Iraq Assistant Professor Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab 140413 India Academician Jamia hamdard Delhi Computer Science Department Al-Turath Universiy College Baghdad Iraq

An emerging threat within blockchain systems is the malicious tactic known as eclipse attacks, wherein attackers aim to deceive and gain control over targeted nodes. Despite the increasing popularity of blockchain technology, effective defence mechanisms against eclipse attacks have been lacking. A novel and dynamic defence model designed explicitly for Proof-of-Work (PoW) consensus blockchain systems is proposed to address this critical issue. The model relies on a node-to-node evaluation mechanism to ensure robust protection. By leveraging mutual evaluations conducted between clients and incorporating the renowned Kademlia algorithm, a ranking value is assigned to each node within the defence model. These rankings empower nodes to prudently select trustworthy peers as neighbours based on a threshold comparison, effectively mitigating the risk posed by malicious nodes. Notably, the proposed approach significantly enhances the security of blockchain systems without necessitating modifications to existing protocols or network structures. The effectiveness of the proposed defence model in countering eclipse attacks is extensively validated through a series of experiments. Furthermore, the approach surpasses previous defence strategies, offering a higher probability of successful defence while maintaining minimal overhead. With its straightforward implementation and operational efficiency, the dynamic defence model is an ideal solution for real-world deployment scenarios. This research presents a compelling and robust defence model against eclipse attacks in PoW blockchain systems, addressing the existing gap in defence mechanisms. Furthermore, this advancement paves the way for the wider adoption of blockchain applications across various domains by providing enhanced security, reliability, and trustworthiness.

关键词： Blockchain Eclipse Attacks Dynamic Defence Model Proof-Of-Work (Pow) Consensus Network Structures Targeted Nodes

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Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation

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Procedia computer science 2024年 233卷 254-268页

作者： N. Mathavan Dr. S. Sivaranjani Assistant Professor Department of Electronics &Communication Engineering Nadar Saraswathi College of Engineering and Technology Vadapudupatti Theni Tamil Nadu India Professor Department of Computer Science and Engineering Sethu Institute of Technology Kariyapatti Virudhunagar Tamil Nadu India

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques .

关键词： Radio Frequency Identification (RFID) Received Signal Strength Indication (RSSI) localization tracking moving target virtual reference tag Spider Monkey Optimization (SMO) Artificial Neural Network (ANN) Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) tracking error tracking accuracy

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Energy efficient refrigeration system using latent heat storage, PCM

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International Journal of Thermofluids 2024年 23卷

作者： Harun-Or-Rashid, Mohammad Hasan, Md Tawfique Alam, Towfiq Bin Hossain, Safayet Associate Professor Department of Mechanical and Production Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Graduate student Department of Mechanical and Production Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh

This study examines the performance enhancement offered by a phase change material (PCM) connected to the evaporator and condenser of a household refrigerator that utilizes R134a as the refrigerant. The features of the refrigerant and its properties determine how quickly a refrigerator releases and stores heat. The use of PCM accelerates heat transfer, leading to an improvement in the Coefficient of Performance (COP). This experimental study demonstrates how the COP of a typical refrigeration system can be significantly improved. In the present study, the PCM is utilized in a manually constructed enclosure that encloses the evaporator and condenser of a refrigerator. The purpose of the PCM is to decrease the condenser temperature, resulting in a substantial reduction of power consumption. The heat is transmitted from the refrigerator cabinet to the evaporator through PCM by conduction. Therefore, the rate of heat transfer from the evaporator to the refrigerant increases noticeably, which enhances the COP of the refrigeration system. From the experiment, it was found that COP increased by 12.7% and compressor off time increased about 1.5 times. © 2024 The Author(s)

关键词： Energy utilization

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Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing

Improving Robotic Arms Through Natural Language Processing, ...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Pascal Sikorski Kaleb Yu Lucy Billadeau Flavio Esposito Hadi AliAkbarpour Madi Babaias Undergraduate Research Assistant Computer Science Department Saint Louis University Saint Louis USA Undergraduate Research Assistant Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA Associate Professor Computer Science Department Saint Louis University Saint Louis USA Assistant Professor Computer Science Department Saint Louis University Saint Louis USA CBL Assistant Professor of Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://***/EnhancedArmEdgeNLP.

关键词： computer vision Mechatronics Source coding Machine vision Large language models Prototypes Focusing Manipulators Natural language processing Edge computing

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A GAN-Based Model of Deepfake Detection in Social Media

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Procedia computer science 2023年 218卷 2153-2162页

作者： Preeti Manoj Kumar Hitesh Kumar Sharma Research Scholar School of Computer Science University of Petroleum and Energy Studies (UPES)Dehradun 248007 India Associate Professor Engineering and Information Sciences University of Wollongong Dubai Knowledge Park Dubai UAE Associate Professor School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun 248007 India

DeepFake uses Generative + Adversarial Network for successfully switching the identities of two people. Large public databases and deep learning methods are now rapidly available because of the proliferation of easily accessible tools online. It has resulted in the emergence of very real appealing fake content that produced a bad impact and challenges for society to deal. Pre-trained generative adversarial networks (GANs) that can flawlessly substitute one person's face in a video or image for that other are proving supportive for implementing deepfake. This paper primarily presented a study of methods used to implement deepfake. Also, discuss the main deepfake's manipulation and detection techniques, and the implementation and detection of deepfake using Deep Convolution-based GAN models. A study of Comparative analyses of proposed GAN with other exiting GAN models using parameters Inception Score “IS” and Fréchet Inception Distance “FID” is also embedded. Along with the abovementioned, the paper discusses open issues and future trends that should be considered to advance in the field.

关键词： Digital Forensics Image Vision Deep Learning Generative adversarial network Deep Fakes Media Forensics Face Manipulation Face Recognition

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