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检索条件"任意字段=2nd IEEE Conference on Information Technology and Data Science, CITDS 2022"

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2022 ieee 2nd conference on information technology and data science, citds 2022 - Proceedings

2022 IEEE 2nd Conference on Information Technology and Data ...

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2nd ieee conference on information technology and data science, citds 2022

ISBN: (纸本)9781665496537

The proceedings contain 45 papers. The topics discussed include: smart watch activity recognition using plot image analysis;single user activity recognition with density activity abstraction graphics algorithm;a multi-round bilinear-map-based secure password hashing scheme;proximity-based anomaly detection in securing water treatment;comparative analysis of deep learning models for network intrusion detection systems;negative sampling in variational autoencoders;leveraging fog computing for geographically distributed smart cities;greedy algorithm for edge-based nested community detection;clustering-based customer representation learning from dynamic transactional data;development of machine learning based model for anomaly detection and fault cause diagnosis of assets in petrochemical industries;evaluation of machine learning methods for predicting rainfall in Bangladesh;machine learning techniques applied to Bangla crime news classification;deep learning-based anomaly detection for imaging in autonomous vehicles;and neural network-based prediction for lateral acceleration of vehicles.

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Predictive Model for Student Career Success Using Blockchain technology and Machine learning 2

Predictive Model for Student Career Success Using Blockchain...

引用

2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Aggarwal, Mansi Vyas, Vaibhav Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computer Science and Engineering- AIML ABES Engineering College UP Ghaziabad India

ISBN: (纸本)9798331530389

Using blockchain for student career prediction offers key benefits, including enhanced data security and privacy through encryption and immutability, ensuring personal information protection. It provides transparency and trust with a tamper-proof record of achievements and skills, fostering confidence among employers and educational institutions. Decentralization eliminates the need for a central authority, reducing risks of data loss or manipulation. Additionally, blockchain enables accurate traceability of student progress, ensuring accountability in career prediction data. Using machine learning to develop a prediction model for student career prediction offers substantial benefits by enabling personalized, data-driven insights that can significantly enhance career guidance. With this vast amount of data from various sources, identifying complex patterns and correlations that may not be evident through traditional analysis. This allows for highly accurate, individualized career recommendations, reducing biases and ensuring fair evaluations. Moreover, this model will continuously improve over time, adapting to new data and refining predictions, ultimately leading to more informed and effective career planning for students. Applying machine learning algorithms on student data stored on blockchain will provide exact information about students including their strengths and weaknesses both. This will lead to a better prediction and effecting planning. © 2025 ieee.

关键词： Prediction models

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Blockchain for Patient-Centric Solutions Using Distributed Ledger technology 2

Blockchain for Patient-Centric Solutions Using Distributed L...

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

作者： Sandhya, G. Vaishnavi, T. Subashree, M. Naveen Kumar, A. Sri Krishna College of Technology Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Blockchain technology, with its decentralized and tamper-resistant nature, has the potential to revolutionize healthcare by improving data security, reliability, and interoperability. By leveraging encryption methods such as Attribute-Based Encryption (ABE), patient records can be securely stored and protected from unauthorized modifications or breaches. Additionally, the integration of smart contracts and standardized data formats enables seamless communication across diverse healthcare systems, fostering a more connected and efficient ecosystem. This patient-focused model enhances data privacy while granting individuals greater control over their medical information. However, the adoption of blockchain in healthcare encounters several challenges, including regulatory hurdles, scalability issues, implementation costs, and acceptance by users. Moreover, integrating blockchain with existing infrastructures and ensuring cooperation among stakeholders adds further complexity. Despite these obstacles, research underscores blockchain's potential to redefine healthcare data management. Addressing these challenges can pave the way for a more secure, interoperable, and patientdriven healthcare system in the future. © 2025 ieee.

关键词： information management

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Intelligent Algorithm System for Security Governance Driven by Big data in Autonomous Systems 2

Intelligent Algorithm System for Security Governance Driven ...

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

作者： Wang, Yiran Shanghai University of Political Science and Law Shanghai201500 China

ISBN: (纸本)9798331505745

With the advancement of big data encryption and decryption technology, the role of data in national governance and social governance has become increasingly prominent. In order to effectively deal with the increasingly complex security governance issues, this study proposes the novel big data-driven intelligent algorithm system. The purpose of this study is to optimize the challenges of the network attack identification, protection measures, and encryption processing of information leakage in security governance through real-time monitoring and predictive analysis of data. The core of the designed system includes two modules: network attack detection algorithm and information leakage modeling encryption algorithm. From a methodological perspective, this study proposes a model for predicting abnormal network traffic based on chaos dynamics. This groundbreaking model uses time series analysis to identify and locate potential DDoS attack traffic. At the same time, combined with information entropy theory, a cascade network attack chain analysis model for DDoS attack protection is designed. At the data security level, an encryption algorithm based on chaos theory is used to effectively protect against information leakage. Through experimental verification, it is known that the proposed encryption algorithm can effectively retain the original data during the information encryption and decryption process. At the same time, through encryption algorithm verification, the security and anti-attack ability of information are improved. © 2025 ieee.

关键词： information leakage

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Towards Designing an information Superhighway for Neurological Disorders 2

Towards Designing an Information Superhighway for Neurologic...

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Mazumdar, Bireshwar Dass Gupta, Prashant K. Singh, Mohak Singh, Sujal School of Computer Science & Engineering Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331530389

Neurological Disorders impact a subject's/person's nervous system in various ways and include a wide range of neurological conditions. More often than not, the impact is life altering and affects the subject's communication skills, social interaction capability, etc. If the condition is detected at an early age, it can significantly enhance the quality of life of the subject with focused training, treatment, etc. Detection through brain scans, behavioral analysis, etc. are costly and time consuming. Recently artificial intelligence (AI) models are being increasingly used for detection & classification of neurological disorders based on various parameters and datasets. However, for an end user, it's still very difficult to realize the power of these models end to end viz., from data acquisition to generating recommendations. Hence, in this paper, we present the architecture of an information Superhighway in the form of AI pipeline. The task of the information Superhighway is to enable capturing the multimodal data (image, video, etc.) of a subject from a smartphone of a laptop webcam, and send this through a secure information channel to a cloud storage. On the storage, the data is managed using various Big data technologies and is acted upon by the DL models. The generated recommendations are sent back through a different secure communication channel back to the mobile or laptop. We feel that our work is first of its kind which makes the technology transfer possible for the end user and utilize full the prowess of powerful AI models. © 2025 ieee.

关键词： Diseases

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AES Efficient Encryption of Multimedia data using Threads for 5G System 2

AES Efficient Encryption of Multimedia Data using Threads fo...

引用

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

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 ieee.

关键词： Sensitive data

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Leveraging RAG for Effective Prompt Engineering in Job Portals 2

Leveraging RAG for Effective Prompt Engineering in Job Porta...

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Haneef, Farhan Varalakshmi, M. Peer Mohamed, P.U. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India CapitalOne United States

ISBN: (纸本)9798331530389

Traditional recruitment methods are time-consuming and heavily reliant on manual processes, leading to inefficiencies. This paper explores the integration of AI technologies into a job portal to streamline the hiring process, focusing on AI's ability to automatically parse and extract information from resumes and job descriptions to match candidates with relevant job opportunities. The platform leverages Large Language Models (LLMs) to extract key information, such as skills, education, and work experience, from both resumes and job descriptions. Fine-tuning through prompt engineering ensures accurate extraction, even when data fields are incomplete or missing. To further enhance matching accuracy, Retrieval-Augmented Generation (RAG) techniques are employed. These mechanisms retrieve relevant information from a structured skills database to provide context, which, combined with the generative capabilities of LLMs, enables more contextually accurate matches. Candidates receive personalized job recommendations based on the information extracted from their resumes, while employers can post job descriptions and use AI-driven tools to match candidates. This approach of using contextual prompts not only improves matching accuracy but also reduces computational time, eliminating the need for custom models tailored specifically to resumes or job descriptions. © 2025 ieee.

关键词： Employment

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Identification of Negative Intent in Social Media Posts Using Natural Language Processing 2

Identification of Negative Intent in Social Media Posts Usin...

引用

2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Tiwari, Priti Verma, Parul HarshDev AIIT Amity University Lucknow India Computer Science and Engineering Department Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798331530389

Daily content viewing and/or posting on social media is very common and influences people across all geographies using numerous strategies and ways. A number of daily activities over web platforms like YouTube in terms of posting, watching, and commenting on videos. This is a report that contains a quickly expanding number of online channels where users post, jot down, and comment on a wide range of topics. NLP is applied to quickly take information from online or web content, that will facilitate your business in very different and numerous ways. Customer-specific data can be easily extracted using NLP, and this data can then be used to create advertising campaigns that are specifically targeted to your customers. This assists in introducing your company to the appropriate target market, which is more likely to convert. The paper will perform the critical analysis of versatile methodologies adapted by various researchers for negative intent identification in social networking sites. The paper will also try to observe the challenges associated with the same. This paper will also analyze the usage of NLP in identifying the negative intent in social media posts. © 2025 ieee.

关键词： Tweets

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Evaluating Self-Supervised Learning Models for Multi-Label Pathology Detection: A Benchmark Against Supervised Learning 2

Evaluating Self-Supervised Learning Models for Multi-Label P...

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Agarwal, Nidhi Pooja Kumar, Nitish Anushka Garg, Yashica Department of Computer Science Engineering Galgotias University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Information Technology Bharati Vidyapeeth College of Engineering New Delhi India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India

ISBN: (纸本)9798331530389

The analysis and objective of the current work is to check the performance of SSL Models in multi-label classification on the available dataset of NIH Chest X-ray images which is a large-scale medical imaging dataset. The performance of SSL models, Dino_v_bl6, Dino with ResNet-50, and MoCo (Momentum Contrast) has been evaluated against the supervised model ResNet. These models are supposed to train to detect multiple pathologies using chest X-ray images. We also note that self-supervised embedding models outperform traditional supervised learning to a significant extent, achieving similar or higher performance on a number of quantitative metrics when compared to the softmax supervised ResNet model. This also draws attention to the possibilities of using SSL for biosignal-specific medical images. Furthermore, SSL approaches offer a promising solution in scenarios where there is an overabundance of annotated datasets available for training models such as deep neural networks. The effectiveness of SSL approaches in addressing the problem of data annotation is also emphasized by this study. © 2025 ieee.

关键词： Self-supervised learning

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NEMO: ML based fish species classification 2

NEMO: ML based fish species classification

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2nd International conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Singh, Manvendra Gaur, Sreesh Tyagi, Sarthak Sharma, Udit Yadav, Prince Mishra, Adarsh Department of Computer Science KIET Group of Institutions Delhi Ghaziabad India Department of CSE-AIML ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331530389

This paper describes the creation of an ML-based tool for image-based identification of fish species commonly found in Indian oceans, addressing the issues that fishermen experience in accurately reporting their catch. Motivated by the Smart India Hackathon (SIH) issue statement from the Indian National Centre for Ocean information Services (INCOIS), our goal is to improve marine fishing advice services. We developed a dataset that includes photos of 21 fish species typically captured by Indian fishermen, precisely chosen to ensure clarity and little background noise. Using this dataset, we applied transfer learning to eight different pre-trained models to determine the most effective one for deployment in a mobile application. The Kotlin-developed program has an easy-to-use interface with catch history, fish nutritional values, and pertinent weather data. Our findings show that the installed models can correctly recognize fish species, with MobileNetV2 reaching a peak accuracy of 97.61%. In addition to expediting the species identification process, this cutting-edge tool provides fishermen with crucial information, which eventually leads to more accurate species-level catch reporting. © 2025 ieee.

关键词： Fish

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