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Artificial Intelligence-based Solution for the Prediction of Power Consumption in Electronics and Software applications

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IETE Journal of Research 2024年第1期70卷 356-371页

作者： C, Savitha Khampariya, Prabodh Singh, Kamred Udham Kumar, Ankit Singh, Teekam Swarup, Chetan Department of Electronics and Communication Engineering Sri Satya Sai University of Technology & Medical Sciences Sehore India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Computer Engineering and Applications GLA University Mathura281 406 India School of Computer Science University of Petroleum and Energy Studies Dehradun India Basic Science College of Science and Theoretical Studies Saudi Electronic University Riyadh-Male Campus Riyadh11673 Saudi Arabia

As new computing paradigms such as mobile grids and clouds become more commonplace, mobile devices are becoming increasingly attractive to scientists and HPC users who need high-performance computing capabilities. There are still many challenges to design software that uses mobile devices, such as their restricted capabilities compared to traditional devices such as PCs and servers. It’s even more critical to remember that batteries are the primary power source for mobile devices. Thus people delete programmes that severely reduce their battery life. The upshot is that it is common for developers to have no idea how much power specific hardware and software components use. So even if they have been trained in software development, they still require specific standards and skills to design energy-efficient apps. This Paper investigates ways to minimize mobile device energy usage by restructuring source code. However, we did not overlook the prospect of applying our findings to other domains, such as recurring demanding computational cores in scientific applications. Specifically, this research contributes to the field of Green Computing and mobile software development. © 2024 IETE.

关键词： Energy efficiency

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Investigating Digital Addiction in the Context of Machine Learning Based System Design 13th

Investigating Digital Addiction in the Context of Machine Le...

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13th International Conference on Innovations in Bio-Inspired Computing and applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Johar, Geetika Patel, Ravindra Department of Computer Science and Engineering UIT RGPV Bhopal India Department of Computer Applications UIT RGPV Bhopal India

ISBN: (纸本)9783031274985

In recent years popularity and availability of digital content are increased due to low-cost internet and the availability of smartphones. The suitable and appropriate use of smartphones will help us in our daily personal and professional life. On the other hand, excessive and extreme use of smartphones may invite different kinds of physical and mental disorders. In this context, we proposed to review recent literature about digital addiction. First, we collected 20 recent research articles about different kinds of addictions and the current state of research trends. Then the role of machine learning in order to deal with addiction has been studied. Further some articles are explored for locating the suitable dataset used, techniques of data collection, machine learning algorithms being applied, area of employment, feature selected, and consequences of the articles. Finally based on the collected experiences of the recent efforts we proposed a Smartphone Addiction (SA) prediction and support framework using the Machine Learning (ML) technique. Finally, the conclusion has been made and future plan has also been discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smartphones

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Hybrid Image Captioning Model

Hybrid Image Captioning Model

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Panigrahi, Lipismita Panigrahi, Raghab Ranjan Chandra, Saroj Kumar Kiit University Department of School of Computer Applications Bhubaneswar India Soa University Department of Computer Science and Engineering Bhubaneswar India Opju University Department of Computer Science and Engineering Raigarh India

ISBN: (纸本)9781665492942

Image captioning is implemented using Deep learning and NLP (Natural Language Processing) resulting in producing a description of an image. The proposed model generates a caption for an image using a Convolutional Neural Network (CNN) together with a Recurrent Neural Network (RNN) and area of attention. Previously, the image names were used as keys to map the images with descriptions. In order to achieve high performance, in the proposed model the image caption is based on the relationship between the areas of a picture (attention model), the words used in the caption, and the state of an RNN language model. The approach of progressive loading is employed for the loading of the image dataset. Further, for encoding the image dataset into a feature vector, VGG16 a pre-trained CNN is used. The extracted feature vector is given as input to the RNN model. These image encodings are output to a specific type of RNN model known as Long Short-Term Memory (LSTM) networks. Subsequently, the LSTM works on decoding the feature vector and predicts the sequence of words, resulting in the generation of descriptions or captions. The training performance is measured using one of the model's quantitative analysis metrics known as BLEU. © 2023 IEEE.

关键词： Long short-term memory

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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

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SN computer science 2024年第8期5卷 1056页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Efficient practical Byzantine Consensus-based reputation method for IoT based electronic waste tracking and tracing system using blockchain

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Multimedia Tools and applications 2024年第37期83卷 84639-84672页

作者： Sharma, Lokesh Gupta, Ravi Kant Lamba, Chattar Singh Kumar, Ajay Lathar, Pankaj Department of IT Manipal University Jaipur Rajasthan303007 India Department of Mechanical Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science & amp Engineering Manipal University Jaipur Rajasthan303007 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science and Applications Delhi Skill and Entrepreneurship University Dwarka New Delhi Delhi110077 India

The utilization of smart contracts enables the integration of blockchain technology to provide incentives for the gathering of electronic trash. Traditional blockchain-based Internet of Things (IoT)-E-Waste Tracking systems have challenges such as data privacy issues, significant delays, and low transaction per second (TPS) rates. In order to tackle these difficulties, the proposed approach utilizes a blockchain-based technology called IoT-enabled Efficient Practical Byzantine Consensus-based Reputation (EPBCR). This approach effectively monitors the post-production business processes of electronic devices by using hybrid consensus and reputation optimization algorithms. As a result, it enhances transparency, traceability, and privacy. The system efficiently regulates and oversees electronic gadgets throughout its supply chain by employing smart contracts on an unchangeable distributed ledger. The methodology is supported by analyzing the gas usage of Smart contracts on the Ethereum network. This results in the creation of, efficient, and high-throughput system that assists participants in conducting economic transactions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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Deep Reinforcement Learning Based Secure Transmission for UAV-Assisted Mobile Edge Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 154-169页

作者： Vijayalakshmi, N. Gulati, Sagar Sujin, B. Ben Rao, B. Madhav Kumar, K. Kiran Department of Computer Science and Applications SRMIST Ramapuram Campus Tamil Nadu Chennai India School of Computer Science and IT JAIN Deemed to be University Karnataka Bengaluru India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman Department of CSE SIR C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility. © 2024 by the authors of this article.

关键词： Deep reinforcement learning

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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SLDERCLC: Improving Smart Learning Frameworks Through Deep Learning Enhanced Recommender Systems Using an Efficient Cross-Layer Collaborative Approach

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SN computer science 2025年第2期6卷 1-18页

作者： Kaur, Harjinder Kaur, Tarandeep Kumar, Mukesh Bhardwaj, Vivek School of Computer Applications Lovely Professional University Punjab Phagwara 144411 India Department of Computer Applications Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India School of Computer Science and Engineering Manipal University Jaipur Jaipur 303007 India

The exponentially scaling domain of a smart learning frameworks necessitates advanced recommender systems capable of providing precise and relevant educational content to learners. Traditional recommender systems often grapple with challenges such as data sparsity, cold start problems, and the inability to capture complex user-item interactions & scenarios. This work introduces a novel approach to recommender systems in smart learning frameworks, addressing these limitations by leveraging deep learning techniques. Our proposed model amalgamates Deep Q Learning with Multilayer Graph Neural Networks (GNNs) for Cross Layer Collaborative Filtering and integrates Autoencoders with Capsule Networks for enhanced recommendation accuracy levels. The use of Deep Q Learning facilitates efficient decision-making in dynamic environments, while Multilayer GNNs adeptly handle relational data, improving the recommendation process by capturing the intricate connections within the educational contents. Furthermore, the incorporation of Autoencoders with Capsule Networks offers a sophisticated mechanism for understanding hierarchical relationships in data, which is crucial for personalized learning paths. The effectiveness of our model is substantiated through rigorous testing on the ASSISTments and EdNet datasets. The results are compelling, showcasing a 4.9% increase in precision, 5.5% improvement in accuracy, 3.5% higher recall, 2.9% greater AUC (area under the curve), 3.4% increased specificity, and a notable 4.5% reduction in delay for smart learning recommendations in comparison with SBBR, DRL and ROME. These improvements highlight the model’s proficiency in delivering timely and relevant educational content, thereby enhancing the learning experience of students as well as the teaching strategies of faculty. The framework attains significant improvements in the efficiency and effectiveness of educational content recommendations thereby increasing the retention rate of students w

关键词： Autoencoders Deep learning Graph Neural Networks Recommender systems Smart learning

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Fake News Detection Using ARO and LSTM Algorithms

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SN computer science 2025年第1期6卷 1-11页

作者： Bhagat, Amit Dangi, Dharmendra Suman, Vaibhav Dixit, Dheeraj Kumar Sharma, Suvarna Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Engineering MITS Madhya Pradesh Gwalior India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

There has been an exponential increase in social media content over the past few years due to the popularity of social media platforms. However, this has also resulted in the dissemination of intentionally false information, or fake news, which can have significant negative effects on society. As a result, detecting fake news has become an important issue that requires attention. Promoting trust in the global community is crucial to preventing the spread of false information in mass media. A study report suggests a reliable technique for identifying false news in order to remedy this. The author has thoughtfully chosen two distinct datasets: the Buzzfeed dataset, which is an assortment of news articles from Buzzfeed News, and the ISOT dataset, which is a collection of social media postings and articles from other sources. The author thoroughly analyzed a variety of content kinds and assessed the efficacy of various approaches and methods for processing and categorizing them using the datasets. The study found that these datasets have accuracy rates of 99% and 97.8% respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ARO algorithm Fake news detection LSTM Pre-processing

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