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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Lakshin Archana, A. Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sea level

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Cross-Domain Collaborative Filtering: A Deep Neural Network Approach for Accurate and Diverse Recommendations 2

Cross-Domain Collaborative Filtering: A Deep Neural Network ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Goel, Chirag Sinha, Bam Bahadur Department of Computer Science and Engineering Indian Institute of Information Technology Jharkhand Ranchi834010 India

The rapid proliferation of data and the intricate nature of user behavior in the online realm have presented new hurdles for recommendation systems, which aim to suggest pertinent items to users. Among the notable challenges, ensuring the provision of accurate recommendations across diverse domains stands out. This research paper proposes an innovative solution to tackle this challenge by developing a cross-domain recommendation system that leverages the collaborative filtering technique to generate precise and varied recommendations spanning multiple domains. The system gathers user behavior and item attribute data from various domains and employs a collaborative filtering algorithm integrated with a deep neural network. The model makes use of a Neural Network with CReLU activation along with the embeddings of the user and the item followed by concealed layers. The output layer employs tanh activation to guarantee that recommendations fall within [-1, 1]. Adam optimizer, MSE loss, and an accuracy metric are utilised in training. This architecture captures user-item interactions effectively, resulting in precise personalised recommendations resulting an accuracy of 96.5% - 97.5%. To validate the effectiveness of the model, extensive evaluations are conducted on real-world datasets encompassing movies and books. The results demonstrate that the proposed system outperforms state-of-the-art recommendation systems in terms of accuracy, as measured by precision(89%), recall(90%), and AUC(0.74) scores. Furthermore, the system exhibits robust performance even in scenarios characterized by cold-start problems and data sparsity. © 2024 Elsevier B.V.. All rights reserved.

关键词： Collaborative filtering

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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A tensile wearable SHF antenna with efficient communication in defense beacon technology

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Defence technology（防务技术） 2024年第11期41卷 198-210页

作者： Pooja Naresh Bhatt Rashmi Pandhare Department of Electronics and Communication Engineering Indian Institute of Information Technology(IIIT)NagpurMaharashtra 440001India Department of Electrical and Electronics Engineering IPS AcademyInstitute of Engineering and ScienceIndoreMP 440001 India

The study projects a flexible and compact wearable pear-shaped Super High Frequency(SHF)antenna that can provide detailed location recognition and tracking applicable to defense beacon *** mini aperture with electrical dimensions of 0.12λ_(0)×0.22λ_(0)×0.01λ_(0)attains a vast bandwidth over 3.1-34.5 GHz Super High Frequency(SHF)frequency band at S_(11)≤-10 dB,peak gain of 7.14 dBi and proportionately homogeneous radiation *** fractional bandwidth(%BW)acquired is 168%that envelopes diversified frequency spectrum inclusive of X band specifically targeted to all kinds of defense and military *** proposed antenna can be worn on a soldier's uniform and hence the Specific Absorption Rate simulation is *** Peak SAR Value over 1 g of tissue is 1.48 W/kg and for 10 g of tissue is 0.27 W/kg well under the safety *** flexibility is proven by analyzing the full electromagnetic simulations for various bending *** response analysis is attained with its Fidelity Factor and Group *** excellence is determined using Link Budget Analysis and it is seen that margin at 100 Mbps is 62 m and at 200 Mbps is 59 *** is fabricated along with experimental *** the results show harmony in shaping the antenna to provide critical situational awareness and data sharing capabilities required in defense beacon technology for location identification.

关键词： Compact Defense beacon technology Link budget SAR Time domain Wearable

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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A Procedure of Weight Thresholding on Complex Networks 3

A Procedure of Weight Thresholding on Complex Networks

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： John, Jisha Mariyam Lekha, Divya Sindhu Indian Institute of Information Technology Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350300604

In recent years, a huge amount of data are available and can be characterized in the form of weighted networks. Analyzing real world weighted networks with large number of elements and links is still a challenging task specifically for extracting relevant features of the networks. The weight thresholding is a simple approach for sparsification of the networks. Here, we tried to define a procedure of weight thresholding based on a fixed fraction of maximum weight. © 2023 IEEE.

关键词： Complex networks

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Investopolis: Decentralized Customer Loyalty Tokenization on the Blockchain

Investopolis: Decentralized Customer Loyalty Tokenization on...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kuppa, Venkata Krishna Paanchajanya Hegadi, Rajendra Sajjan, Karthik Chowdary, Karusala Deepak Vanga, Manoj Sahit Reddy Kumar, Poonam Indian Institute of Information Technology Dharwad India Indian Institute of Information Technology Computer Science and Engineering Dharwad India IIIT Dharwad India IIM Sambalpur India

ISBN: (纸本)9798350324136

The tokenization of customer loyalty on the blockchain has emerged as a promising research area, offering numerous advantages over traditional loyalty systems. By leveraging fungible tokens on a blockchain network, companies and marketplaces can reward customers for their investments and purchases. In this study, we present the design and implementation of 'Investopolis,' a decentralized application that integrates customer loyalty through blockchain technology. Investopolis serves as an investment platform, enabling investors to invest in companies conceptualized by innovators that are not yet listed in the stock market but hold substantial market value potential. This research sheds light on the potential of leveraging blockchain technology for customer loyalty and highlights the benefits of decentralized platforms in the investment landscape. © 2024 IEEE.

关键词： Blockchain Customer loyalty ERC20 Ethereum Fungible tokens Ideators

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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

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Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and information technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of Electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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Stock Indexes Community Identification Using BAT-Modified Optimization Algorithm

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SN computer science 2024年第8期5卷 1-14页

作者： Aggarwal, Kirti Arora, Anuja Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock price accelerates interest and preference of the young generation to explore the stock market with elicit interest. An autopilot system is needed where users choose beneficial stocks of their choice without paying attention to the manual inspection or implication. This study aims to form highly correlated stock communities based on fluctuations in stock price which will be helpful in the prediction and decision-making of users about their expenditure in the stock market. For the enormous volume of stock price time series data, the opted data representation method is the network which is generated with the help of a data statistical correlation test. Another research challenge was to detect the community in an optimized manner due to complex stock networks. For the same, BAT optimization algorithm and a proposed BAT-modified (BAT-M) algorithm are applied for complex stock network community formation. The better community formation results showcase that BAT-M gives better performance in comparison to the standard BAT algorithm on various performance evaluation factors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BAT Community detection Correlation Modularity Nature-inspired Optimization Stock indexes Stock network

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