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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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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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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...

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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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Performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions 2

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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

作者： Upadhyay, Deepak Prakhar Moray, Rashmy Sharma, Rashmi Venu, Nookala Shukla, Shipra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India A constituent of Symbiosis International Deemed University India Nagpur Campus Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798331530389

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions. © 2025 ieee.

关键词： Adaptive modulation

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Predictive Analysis of Kidney Diseases with Artificial Intelligence and data science

Predictive Analysis of Kidney Diseases with Artificial Intel...

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2025 International conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Eswar, G. Hariharan, C. Lavein, S. Akilandeswari, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331521394

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse. © 2025 ieee.

关键词： Supervised learning

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A 51.6TFLOPs/W Full-datapath CIM Macro Approaching Sparsity Bound and -30Loss for Compound AI 72

A 51.6TFLOPs/W Full-Datapath CIM Macro Approaching Sparsity ...

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72nd ieee International Solid-State Circuits conference, ISSCC 2025

作者： Yue, Zhiheng Xiang, Xujiang Wang, Yang Guo, Ruiqi Han, Huiming Wei, Shaojun Hu, Yang Yin, Shouyi Tsinghua University Beijing China

ISBN: (纸本)9798331541019

Large-language models (LLM) are widely used with exceptional performance, but their prohibitive size and cost limits deployment on edge devices. The compound-AI combines several specialized small models to achieve matched or even superior accuracy on target downstream tasks [1,2]: e.g., RevCol [3] fuses multiple convolutional models and surpasses the 7.2B-parameter monolithic LLM model [4] on the ImageNet classification task by 1.64%. Therefore, the shift to compound systems opens opportunities for edge deployment in addition to model-size scaling. SRAM-based floating point (FP) CIM promises accelerated edge-AI models [5-14]. Figure 14.4.1 shows a conventional FP CIM macro, which faces three challenges for compound-AI acceleration: (1) Previous FP CIMs rely on a Gaussian data distribution to reduce alignment loss, where data is distributed around its mean [11]. However, compound-AI employs diverse models, with multiple data distributions, leading to an accuracy degradation of more than 5% [15]. (2) In variable compound-AIs, the CIM only performs MAC operations [16-23];however, partial-sum/residual accumulation requires data movement and accounts for 26-72% power consumption, especially for bit-serial CIMs. (3) Conventional CIMs explore sparsity according to specific model features [11,12], but compound-AI models have flexible sparse patterns, causing a gap between the actual and theoretical speedup (1/1-sparsity). © 2025 ieee.

关键词： Artificial intelligence

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