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6th International Conference on Smart Computing and informatics, SCI 2024

作者： Sindhu, B. Madhu, Shrija Taruni Chowdary, G. Sai Venkata Abhiram, D. Venkatesan, K.G.S. Rajamahendravaram India Department of Computer Science and Engineering Megha Institute of Engineering & Technology for Women Telangana Hyderabad India

ISBN: (纸本)9789819619801

In the present scenario, the accessibility of proper medical resources has reached a critical juncture, characterized by a scarcity of medical professionals and medical supplies in several geographical areas majorly in rural and semi-urban areas. Majority of uneducated and even educated people have opted to self-medication without proper professional guidance which can make their health conditions much worse. Capitalizing on the advancements in Machine Learning and its growing role in various domains, this study introduces an innovative drug recommendation system intended to reduce the burden on medical practitioners and help the society. The drug recommendation system works like an expert who can prescribe apt medicines. It assesses patients who have consumed specific drug and it uses mathematical and logical approaches to find out which drugs are more likely to work well based on given symptoms. The proposed system eradicates false suggestions by implying TF-IDF algorithm on the reviews to extract the essence of it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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A Hybrid Embedding Approach to Classify Crisis-Related Short Text 12th

A Hybrid Embedding Approach to Classify Crisis-Related Short...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Nath, Basudev Sahoo, Deepak Paul, Nayan Ranjan Faculty of Engineering Technologies Sri Sri University Cuttack India Department of Computer Science and Engineering Silicon Institute Technology Bhubaneswar India

ISBN: (纸本)9789819788354

High mobile phone usage and internet access on phones make it simple to connect to the globe and share your thoughts, feelings, and views on local or global issues on social media. This social media content helps government and corporate institutions grasp public opinion. People utilise social media to report disasters and request medical, food, and water aid. It is difficult to recognise disaster-related tweets and determine people's needs. This paper proposed a hybrid model to recognise disaster-related tweets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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FabricSense: A Tactile-Based Fabric Structure Detection Methodology for Automatic Temperature Adjustment

FabricSense: A Tactile-Based Fabric Structure Detection Meth...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Dayananda, Chaithanya Hemashree, P. Harshini, M. Sai Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Annamcharya Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350302820

Based on tactile information and content-based image retrieval (CBIR), this methodology proposes a novel way for fabric structure detection and automatic temperature control. The technology accurately identifies fabric structures and establishes the ideal ironing temperature by combining tactile qualities, visual elements retrieved from fabric photographs, and programmed thermostat sensors. Unlike standard visual-based methods, this approach analyses colour, texture, and shape properties of fabric samples using tactile information and CBIR algorithms, enabling reliable fabric structure detection. Through testing and improvement, the system's performance is confirmed, providing exact temperature adjustment and improving the ironing experience. © 2023 IEEE.

关键词： Iron

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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Security Preserving Distributive n-Party Computation 38

Security Preserving Distributive n-Party Computation

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38th IEEE Region 10 Conference, TENCON 2023

作者： Dutta, Mou Dhal, Subhasish Saxena, Ashi Sarkar, Mahasweta Indian Institute of Information Technology Department of Computer Science & Engineering Assam Guwahati India Dr. Akhilesh das Gupta Institute of Technology and Management Department of Computer Science & Engineering India San Diego State University Department of Electrical & Computer Engineering United States

ISBN: (纸本)9798350302196

In n-party Computation, data from different sources are integrated to achieve a common goal, which can be beneficial for different applications. However, this goal may be at risk due to the fact that this corresponding data can be the target of attacks from outside attackers as well as inside ones. On the other hand, in Secure n-party Computation, the data can be processed by preserving security and privacy in such a way that no party can know the data of the others. Several schemes have been proposed to address the issues regarding n-party computation. However, some of them are not efficient in terms of computation and communication, or some of them have used a Trusted Initializer (TI), a third party, which is considered as trusted, for communicating between different parties. Therefore, to address these issues, we propose an n-party secure computation protocol without using any Trusted Initializer (TI). We formally simulate our protocol using Automated Verification of Internet Security Protocols and Applications (AVISPA) as well as we elaborate on the formal and informal security analysis of our proposed protocol. Performance analysis of our proposed protocol is also carried out and it is observed our proposed protocol is secure and protects the privacy of the system. © 2023 IEEE.

关键词： Privacy-preserving techniques

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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Data-driven strategies for digital native market segmentation using clustering

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 178-191页

作者： Uddin, Md Ashraf Talukder, Md. Alamin Ahmed, Md. Redwan Khraisat, Ansam Alazab, Ammar Islam, Md. Manowarul Aryal, Sunil Jibon, Ferdaus Anam School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia

The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market. © 2024 The Authors

关键词： Cluster analysis

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Parkinson’s Disease Prediction Using Machine Learning and Nature-Inspired Optimization Technique 5th

Parkinson’s Disease Prediction Using Machine Learning and N...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Mittal, Ayushi Tayal, Devendra Kumar Gupta, Charu Srivastava, Neha Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women Kashmere Gate New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

ISBN: (纸本)9789819760350

Parkinson’s disease (PD) is a nervous system condition that gradually becomes worse with the passing of time and is caused by injury to brain cells responsible for producing dopamine. When between 60 and 80% of dopamine-producing are lost, the production of dopamine diminishes, leading to the manifestation of motor symptoms associated with PD. Artificial intelligence (AI), particularly machine learning (ML), has become increasingly pivotal in medicine. Optimization techniques play a vital role in enhancing the accuracy and efficiency of disease prediction models. In the proposed work, we have implemented a nature-inspired firefly optimization technique to see its results for the correct disease classification. It is implemented using KNN, SVM, decision tree, and random forest combined with a firefly algorithm for the classification while minimizing the features selected. The highest results achieved are using random forest as a classifier, with a maximum accuracy of 87.22%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random forests

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Establishing Trust in Online Voting: Blockchain Solutions for Secure Elections with Immutability and Efficiency 2

Establishing Trust in Online Voting: Blockchain Solutions fo...

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

作者： Pandey, Ashwani Kumar Tripathi, Abhinandan Yadav, Ashok Kumar GIDA Buddha Institute of Technology Department of Computer Science and Engineering UP Gorakhpur India Rajkiya Engineering College Department of Information Technology UP Azamgarh India

ISBN: (纸本)9798350368109

Today, various emerging applications widely use blockchain technology (BT) as a backbone platform to ensure security, privacy, and efficacy. Blockchain is a dispersed, inalterable, unambiguous, defended, and open cryptographic secure ledger technology. A wide number of blockchain applications in several sectors can be built using some of the technology's most important features, such as security, speed, immutability, persistency, anonymity, and decentralized distributed ledgers. Blockchain technology (BT) offers huge potential for storing, accessing, and preserving documents. One of the most well-known blockchain applications is the online voting system. Most of the e-voting systems rely on centralized servers, and voters need to trust the election authorities for the precision of polling results. This article proposes an enhanced blockchain-enabled decentralized voting system. The proposed blockchain-based voting system will be secure and enhance public trust. Data safety and transparency are key features of the system. The system guarantees a single vote for each unique identity in each poll, while also guaranteeing obscurity. To affirm transparency, integrity, and stability, the recommended model has used EVM to write smart contracts for each voting event. It allows users to vote inconceivably from any location and at any time by using digital devices. © 2024 IEEE.

关键词： Distributed ledger

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