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2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Kumar, Anurag Singh, Kalyani Raj, Aman Shakya, Aryan School of Computer Applications and Technology Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350366051

With the use of several Python modules, this project gives us a general idea of how to forecast home prices using several machine learning models. This suggested model offers the most accurate prediction and is thought to be the most accurate model for determining home prices. This gives a quick overview that is necessary to forecast the cost of a home. Using the datasets we will utilize in our proposed model, this study aims to explain what and how the house pricing model functions with the aid of machine learning techniques. Finding the right moment to purchase a property and figuring out The purchase price of a house in a certain area is helped by a price *** objective of this machine learning task on house price prediction is to develop a machine learning model based on data in order to forecast house. © 2024 IEEE.

关键词： Prediction models

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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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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity 3

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Nayak, Deepika Nair, Rekha R Tinababu Kishore, S. Alliance University Department of Computer Science and Engineering Alliance School of Advanced Computing Bengaluru India Department of Computer Applications Karnataka Mangaluru India

ISBN: (纸本)9798350354171

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was 97.58%. The system's application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture Institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole. © 2024 IEEE.

关键词： Agribusiness

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Classification of Skin Cancer Using Deep Learning 8th

Classification of Skin Cancer Using Deep Learning

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8th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2023

作者： Nagarjun, A. Manju, N. Aradhya, V. N. Manjunath Shruthi, N. Malapriya, S. Department of Information Science and Engineering JSS Science and Technology University Mysuru India Department Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9789819976324

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available, early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10,015. Transfer learning technique is used to train the model to obtain outstanding results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI)

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Neural Computing and applications 2025年第17期37卷 10949-11000页

作者： Nandan, Mauparna Mitra, Soma De, Debashis Department of Computer Applications Techno Main Salt Lake West Bengal Kolkata India Department of Computational Science Brainware University West Bengal Barasat India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal West Bengal Kolkata India

Graphs find wide applications in numerous domains, ranging from simulating physical systems to learning molecular fingerprints, predicting protein interfaces, diagnosing diseases, etc. These applications encompass simulations in non-Euclidean space, in which a graph serves as an ideal representation, and are also an indispensable means of illustrating the connections and interdependencies among its various constituents. Graph neural networks (GNNs) are specific types of neural networks that are specifically built to handle data possessing a graph structure. They are highly effective at capturing intricate relationships among different entities. Nonetheless, their "black-box" characteristics pose difficulties in transparency, trust, and interpretability, especially in critical sectors like heath care, banking, and autonomous systems. Explainable artificial intelligence (XAI) has emerged to clarify these obscure decision-making processes, thus enhancing trust and accountability in AI systems. This survey paper delves into the intricate interplay between GNNs and XAI, including an exhaustive taxonomy of the various explainability methods designed for graph-structured data. It classifies the existing explainability methods into post hoc and self-interpretable models. The paper analyzes their practical applications in diversified fields, highlighting the significance of transparent GNNs in essential sectors such as fraud detection, drug development, and network security. The survey also delineates evaluation parameters for assessing explainability along with addressing persistent issues in scalability and fairness. The paper concludes by addressing prospective advancements in the subject, including the creation of innovative XAI methodologies tailored for GNN architectures, integration with federated learning, and utilization of these models in interdisciplinary fields. This study bridges the gap between GNNs and XAI, providing an essential resource for researchers and p

关键词： Graph neural networks

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Factor Graph Neural Network Meets Max-Sum: A Real-Time Route Planning Algorithm for Massive-Scale Trips 23

Factor Graph Neural Network Meets Max-Sum: A Real-Time Route...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Li, Yixuan Wang, Wanyuan Xu, Weiyi Deng, Yanchen Wu, Weiwei Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University Nanjing China

Global route planning (GRP) is a typical combinatorial optimization problem that has been solved for a variety of industrial purposes, such as traffic flow management, network routing, and conflict prevention. The goal of the GRP is to find a route for each trip query such that all queries have a minimum global travel time. The GRP problem is NP-hard and computationally challenging, even for medium-sized instances. However, in real-world GRP applications, such as Google Maps-based vehicle route guidance systems, there are always massive-scale trips issued simultaneously, and real-time response is required. Existing mathematical programming-based exact methods and heuristics struggle to balance the extremes of optimality and scalability. Considering that many closed-related GRP instances must be solved repeatedly, this paper explores a deep learning approach to learn real-time and efficient solutions for GRP. This paper first proposes a novel route-query factor graph (RQ-FG) to model the GRP problem, where the message-passing damped Max-sum (DMS) algorithm can be exploited to generate high-quality approximate solutions. A hybrid pruning method is proposed to accelerate solving the DMS. We further devise a route-query factor graph neural network (RQ-FGNN) based on the RQ-FG, which has the ability to return solutions in milliseconds. Experiments demonstrate that our method can generate high-quality solutions in massive-scale GRP instances in real-time. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Message passing

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Prediction of Efficient Driving Mode for Vehicle using Machine Learning 3

Prediction of Efficient Driving Mode for Vehicle using Machi...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Hegde, Ramakrishna Kripa Das, Sujan D Sriraksha, M.S. Siri, R. Siri Gowri, H. Soumyasri, S.M. Vidyavardhaka College of Engg. Computer Science Engineering Mysuru India PES University Computer Science Engineering Bengaluru India The National Institute of Engineering Master of Computer Applications Mysuru India

ISBN: (纸本)9798350340358

The field of autonomous driving cars is a rapidly growing area of research, machine learning and image recognition technology plays a pivotal role in ensuring their safe navigation on the roads. Different driving modes are offered by modern cars to accommodate various road conditions and driver preferences. However, manually selecting these modes can be cumbersome. Addressing the dynamic nature of mode switching in autonomous driving systems presents a multifaceted challenge. The system must continuously assess a multitude of variables such as traffic patterns, road conditions, weather conditions, and the wear and tear of vehicle components. Traffic conditions can change rapidly, requiring real-time analysis of traffic flow and congestion. Road conditions, including the presence of obstacles, construction zones, or slippery surfaces, demand instant adaptation for safe navigation. Weather conditions further complicate the scenario, as rain, snow, fog, or even intense sunlight can impact visibility and road grip. Addressing these challenges requires sophisticated algorithms capable of processing vast amounts of data from various sensors in real time. Machine learning models must be trained to recognize patterns in diverse conditions, enabling the system to make informed decisions swiftly and accurately. Moreover, the system must possess the ability to prioritize these factors based on their immediate impact on driving safety and efficiency. This can be a future enhancement of the paper. By leveraging machine learning algorithms, the mode selection process can be automated. The system constantly monitors driving conditions, learns from real-time camera sensor data and selects the most optimal mode to adapt to current situation. Different models such as Convolutional Neural Network (CNN), CNN with MobileNetV2 as base and decision trees are analyzed. © 2023 IEEE.

关键词： Decision trees

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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A differential approach and deep neural network based data privacy-preserving model in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4659-4674页

作者： Gupta, Rishabh Gupta, Ishu Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

Data outsourcing has become indispensable to allow information sharing among multiple parties. The users do not fully trust the cloud platform since it is operated by a third party. Preserving privacy while sharing the data among different parties is a challenging task;therefore, users apply the differential privacy mechanism to protect their data. However, such protection mechanisms suffer from the problem of degradation of learning results. In this paper, the authors address the degradation of the learning results due to noise injection into user’s data through ϵ-differential privacy. A differential approach and deep neural network based data privacy-preserving model is proposed, which injects noise at an appropriate position by exploiting the properties of the Laplace transform to maintain the accuracy level. The experiments are conducted over Steel Plates Fault, Spambase, Banknote Authentication, and Monk Problem datasets for deep neural network classifier to evaluate the model’s efficiency in accuracy, precision, recall, and F1-score terms. The achieved results show that the proposed model ensures high accuracy, precision, recall, and F1-score up to 99.75%, 99.72%, 99.72%, and 99.72%, and improvement up to 19.34%, 30.67%, 29.39%, and 32.11%, respectively, as compared to existing approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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