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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shree, Ritu Narmadha, T. Joseph, Vishakha Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Arka Jain University Department of Management Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs. © 2024 IEEE.

关键词： Benchmark Consequences Convolution Photographs Reinforcement Representation

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A Survey of Dynamic Exploration Symmetric Encryption with Backward & Forward Secrecy 3

A Survey of Dynamic Exploration Symmetric Encryption with Ba...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Satendra Bhagat, Samriti Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

A sophisticated type of symmetric encryption is called Multiuser Dynamic Searchable Symmetric Encryption. It expands the capabilities of conventional symmetric encryption to enable actions such as adding and removing encrypted data as well as the ability for a verified set of information users to access their preferred encrypted data within the dynamic database. The latter can use using erased data and search results to comprehend the content, while the former allows attackers to discover by performing file injection attacks, keywords from the injected data were exposed. can do. To our knowledge, the available literature has not completely addressed these privacy problems for M-DSSE. In light of this, our attention is directed toward multiuser dynamic searchable symmetric encryption that satisfies the requirements for forwarding and backward security. It uses Inverted indexes are created and updated using random number generator Functions (RNG) and Authenticator primitives search tokens to suggest specific solutions. In the accidental model, the suggested approach is shown to be secure. Additionally, the performance study demonstrates that the suggested approach provides increased security assurances at a fair cost to efficiency. © 2023 IEEE.

关键词： Cryptography

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Electroencephalography (EEG) Based Prediction of Learning and Responsiveness among Students 7

Electroencephalography (EEG) Based Prediction of Learning an...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Agrawal, Alok Kumar Ahmed, Bilal Jakhar, Yogesh Kumar Bajaj, Karan Kumar, Girish Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University India University of Engineering & Management Department of Computer Application Jaipur India

ISBN: (纸本)9798350340600

The constructivist method of teaching has been shown to have a major influence on the learning outcomes of students. In order to improve classroom instruction, it is essential to evaluate students' levels of involvement and attentiveness while they are being taught. Electroencephalography (EEG) can now be used to assess students' attentiveness in real time thanks to recent neuro-engineering breakthroughs and mobile brainwave sensors' availability. The purpose of this study is to provide an overview of EEG-based techniques with the intention of predicting constructivism and engagement among students of varying levels. The purpose of this study is to evaluate the efficacy of these approaches and to determine the extent to which they have the potential to enhance the design of future learning systems. This survey analyzes whether or not approaches based on EEG may be used to determine the level of constructivism and involvement displayed by pupils. The findings provide helpful insights that can be used to develop tailored teaching strategies and improve education's overall efficiency. However, some obstacles need to be overcome in order for EEG-based predictions to be accurate. Some of these obstacles include data quality and variability, individual differences, interpretability, generalization, and ethical considerations. © 2023 IEEE.

关键词： Forecasting

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IOT-based Smart Ground water Recharge System 1

IOT-based Smart Ground water Recharge System

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Uniyal, Manas Bhatt, Chandradeep Mohd, Noor Kumar, Devendra Graphic Era Hill University Computer Science Department Dehradun India Abes Engineering College Computer Application Department Ghaziabad India

ISBN: (纸本)9798350338027

Water is the most important commodity for the existence of life. In India, the main sources of water are rainwater(monsoon) that fills the rivers with water and underground water. However, we have seen a steady decline in underground water levels across India at an alarming rate that affects the agriculture and daily water needs of the citizens. This paper suggests a smart ground water recharge system that automatically recharges the ground water after rain falls. The system uses smart IOT devices, solar panels, and a filter to filter out unwanted materials that come along the rainwater. In this paper a fully automatic rainwater harvesting is introduced that collects rainwater and filters out dirt and unwanted materials(trash) that comes along the rain. It also senses the acidic content of rain and filters it out when needed. Later on, the filtered water can be directly recharged in ground, or it can also be collected in artificial pools for irrigation purposes. Polluted water can be very harmful for our environment and food cycle. © 2023 IEEE.

关键词： Aquifers

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Exploring the Benefits of Interactive Communication Media Algorithms for Networking applications 3

Exploring the Benefits of Interactive Communication Media Al...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ahmed, Zahid Beemkumar, N. Ghosh, Debanjan Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications. © 2024 IEEE.

关键词： Behavioral research

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Lifestyle Classification Amidst Psychological Impact of COVID: A Machine Learning Approach 7

Lifestyle Classification Amidst Psychological Impact of COVI...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

ISBN: (纸本)9798350340600

The COVID pandemic has caused significant social changes, affecting not just physical health but also individuals' psychological well-being. This study investigates the complex interaction between lifestyle choices and the psychological impact of the pandemic, using a Machine Learning (ML) technique to categorise and assess distinct lifestyle patterns in the context of COVID. In this study, a significant data from a variety of sources, including questionnaires, social media, and healthcare records, to get insight into how the pandemic has affected lifestyle characteristics such as exercise, food, social connections, work habits, and leisure activities. This study has analysed the psychological effects of these lifestyle changes, including stress, anxiety, sadness, and overall mental health. This research article implements the basic classification models like (Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, and XGBoost), which can classify various lifestyle changes among individuals based on their pandemic-related behaviours. The impact of these lifestyle changes on mental health outcomes is investigated, offering light on the intricate interplay between personal decisions and psychological well-being during times of crisis. © 2023 IEEE.

关键词： Machine learning

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Alzheimer's Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer's Disease Prediction from MRI image using Convolut...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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ChatGPT Based Image Steganography (CGIS): A Novel Intelligent Information Hiding Approach to Achieve Secure Covert Communication 1

ChatGPT Based Image Steganography (CGIS): A Novel Intelligen...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Mukherjee, Subhadip Mukhopadhyay, Somnath Sarkar, Sunita Assam University Department of Computer Science and Engineering Silchar788011 India Sikkim University Department of Computer Application Gangtok737102 India

ISBN: (纸本)9798350342796

Covert communication refers to the practice of exchanging information or messages in a discreet or secretive manner, with the intent of keeping the communication hidden from unintended or unauthorized recipients. Steganography is a widely used, strategy for establishing covert communication, for maintaining the privacy and security of sensitive information. Steganography ensures that the message remains concealed from surveillance or eavesdropping. Artificial Intelligence (AI) plays a crucial role in enhancing security across various domains of covert communication. ChatGPT is an AI model, specifically a natural language processing (NLP) model. It belongs to the broader category of AI models which can comprehend and produce language for humans. In this paper, a new image steganographic model named CGIS is developed for covert communication using ChatGPT. Proposed steganographic method has achieved 3.0 bpp of hiding capacity. In situations where confidentiality is critical, such as in military operations, intelligence agencies, or corporate strategies, proposed method ensures that the message cannot be detected and or extracted by any surveillance or eavesdropper. © 2023 IEEE.

关键词： Artificial Intelligence CGIS ChatGPT Covert Communication Image Steganography

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