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

作者： Singla, Monika Gupta, Deepali Goyal, Deepam Dang, Rajeev Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh group of colleges Chandigarh School of Business Department of Computer Application Jhanjeri140307 India UIET PUSSGRC Department of Mechanical Engineering Punjab Hoshiarpur India

ISBN: (纸本)9798350370249

Farming encompasses the act of cultivating land, growing crops, and raising animals, which is crucial for the growth of country's economy. For every country, agriculture contributes to approximately 70% of the main source of income. Historically, farmers have used traditional agricultural methods, which though time-consuming, often yielded imprecise results, leading to decreased output. Precision agriculture stands out as a viable solution to this issue, optimizing production by accurately identifying the necessary measures at an appropriate time. Elements of precision farming include meteorological prediction, soil assessment, crop recommendation for cultivation, and determination of optimal quantity of fertilizers and pesticides. Precise farming utilizes sophisticated techniques such as Machine learning, IoT, Data analytics and Data Mining to gather data, train systems, and forecast outcomes. Precision farming utilizes technology to minimize manual effort and enhance output. Recent agriculture challenges, such as crop failures because of low rainfall and soil sterility, raise the need for further exploration. This article aims to explore efficient methods used for crop management and harvesting in response to environmental changes. Several machine learning and deep learning models have come out with encouraging results in crop disease detection, thereby enhancing crop quality and productivity. © 2024 IEEE.

关键词： Smart agriculture

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Cash Flow Forecasting Based on Wavelet Transform and Neural Networks 8

Cash Flow Forecasting Based on Wavelet Transform and Neural ...

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8th International Conference on computer Science and engineering, UBMK 2023

作者： Soylen, Zumra Mammadova, Farida Fasounaki, Mandana Ozmen, Ayse Irem Ince, Gokhan Istanbul Technical University Department of Computer Engineering İstanbul Turkey Mdp Group R&d Department İstanbul Turkey Istanbul Technical University Department of Computer Engineering & Ai and Data Science Application and Research Center İstanbul Turkey

ISBN: (纸本)9798350340815

Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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Using Trust-Based Data Integrity Verification and Energy-Aware Hierarchical Data Aggregation for WSN 11

Using Trust-Based Data Integrity Verification and Energy-Awa...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Dushyant Gaganpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9781665487320

Because of their numerous uses, wireless sensor networks are currently quite popular. These nodes are typically placed in severe environments and have limited power supplies, making it difficult to replace them. Because of these problems, reducing energy consumption is a top priority to increase network lifespan. To solve the challenging issue of data aggregate, we created a novel combining technique that carries out grouping & trusted computation procedures. This design divides the networks into three categories based on remaining energy: normal networks, advance networks, & super networks. The proposed model utilises a hierarchical structure, and we present a novel method for determining the best number of cluster. the clustered head of choice. After selecting the root node, we employ a confidence calculation strategy that offers sense confidence, connection trust, & nodes trust. The nodes trust is computed using indirect & direct trust. To protect the data integrity, this confidence mechanism is used hop by hopping. The empirical study shows that the proposed approach boosts efficiency while preserving WSN's safeguards. © 2022 IEEE.

关键词： Energy utilization

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A Survey on Scalable Resource Allocation in Cloud Computing 4th

A Survey on Scalable Resource Allocation in Cloud Computing

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4th International Conference on Recent Innovations in Computing, ICRIC 2021

作者： Bhagat, Sanjit Gupta, Punit Department of Computer Application Manipal University Jaipur Jaipur India Department of Computer Communication and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811682476

The cloud is changing the way of working of small businesses or large enterprises. Utilizing the cloud is becoming trend for everyone as it provides better data storage, flexibility, and security in comparison of the traditional approaches. The cloud provides software’s as well as hardware’s as a resource via the internet through remote servers. Resource allocation is a key factor as it is used to determine the resource utilization, energy efficiency, and feasibility of the data center. The suitable allocation of resources is one of the most important problem in resource optimization as it impacts all the other factors (i.e., energy, cost, time, etc.). This paper provides a comparative study of different issues related to the cloud computing system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

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Asian Journal of Civil engineering 2025年第2期26.0卷 471-489页

作者： Pande, Prashant B. Dhengare, Sagar W. Raut, Jayant M. Bhagat, Rajesh M. Bahoria, Boskey V. Shelke, Nilesh Upadhye, Sachin D. Vairagade, Vikrant S. Department of Civil Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Computer Science and Application Ramdeobaba University Maharashtra Nagpur 440013 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India

The need for sustainable and resilient composite construction materials that can cope with increasing environmental and structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material properties for the composites. To overcome these constraints, the present study develops an innovative multimethod framework by integrating the implementation of several state-of-the-art optimization and machine-learning techniques in order to enhance the sustainability and resilience of composite materials. The work is initialized by proposing a Multiple Objective Genetic Algorithm (MOGA) with dynamic fitness functions for the optimization of material designs, by balancing environmental impacts with mechanical performance in real time. This approach, hence, fits different environmental conditions and material requirements at the same time while importantly enhancing the design stage itself. At the same time, Gaussian Process Regression is the method that enables future LCA outcome prognoses undertaken using RL;it is possible to deal with the sustainability prediction as uncertain, and hence it is incorporated in the ongoing process of material optimization. In this way, RL will adaptively optimize processing parameters for the manufacturing of composites: both material resilience and goals regarding sustainability are realized through self-learning. Finally, a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm is introduced to probe and polish the solution space for composite material designs to leap over local optima hurdles. The overall improvement in the integrated attributes is 15% of the carbon footprint decrease, 20% in the tensile strength, and 12% decrease in energy consum

关键词： Composite Material Design Life Cycle Assessment Multiple Objective Genetic Algorithm Reinforcement Learning Sustainable Materials

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Examining driver sleepiness based on his visual actions with ML Techniques

Examining driver sleepiness based on his visual actions with...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Rawat, Vandana Patwa, Kaushal Negi, Harendra Singh Sharma Ghai, Anupriya Graphic Era Deemed to be University Dept. Of Computer Scienceand Engineering Dehradun India Graphic Era Deemed to be University Dept. Of Computer Application Dehradun India Graphic Era Hill University Dept. Of School of Computing Dehradun India

ISBN: (纸本)9798350378092

Driver drowsiness is a significant risk factor for road accidents. It is essential for road safety to develop efficient sleepiness detection systems. This work concentrate around analyzing driver sluggishness in view of visual activities, expecting to recognize solid marks of To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are drowsiness utilizing PC vision methods. To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are extracted from real-time video footage captured by an onboard camera using a combination of image and video processing algorithms. To prepare and assess the sluggishness location model, a different dataset of drivers in different conditions of languor is gathered, including both reproduced and genuine situations. The dataset is clarified with ground truth marks showing various degrees of languor, got through physiological estimations, driver self-reports, and master assessments. © 2024 IEEE.

关键词： Eye movements

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AI for Disaster Management: Predictive Models and Response Strategies

AI for Disaster Management: Predictive Models and Response S...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shameem, A. Saroja, Devupalli Hotkar, Damodar S. Nayak, Bijaya Bijeta Pandey, Anand Tripathi, Sachin Amet University Amet Business School India Department of Computer Science and Engineering Visakhapatnam India R.T.E. Society's Rural Engineering College Department of Computer Science and Engineering Karnataka Hulkoti582205 India Kiit Deemed to Be University School of Mechanical Engineering Odisha Bhubaneswar India Sharda University Computer Science and Application U.P Greater Noida India Wathoda Symbiosis Law School Nagpur Maharashtra Nagpur India

ISBN: (纸本)9798331508685

The application of artificial intelligence (AI) in managing severe weather events has become more necessary in recent years owing to their increased frequency. Disaster management is required to control and avoid such occurrences. To successfully aid in better and quicker reactions to catastrophes, AI is widely employed in prediction and disaster preparation, damage mitigation and minimization, and rescue phases. To determine how AI technology can be used to lessen the damaging impacts of different disasters, this article looks into the potential for connecting artificial intelligence methods based on technological innovation. The study potentially contributes in designing an effective framework for disaster management based on the predictive accuracy and response time. © 2025 IEEE.

关键词： Disaster prevention

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Stage Wise Classification and Prediction of Non-Alcoholic Fatty Liver Disease using Hybrid Approach 2

Stage Wise Classification and Prediction of Non-Alcoholic Fa...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prabagaran, A. Balamurugan, N.M. Kalavathi, S. Padmanabhan, P. Angelin Blessy, J. Gupshup Technology India Pvt Ltd Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Information Technology Chennai India Noida Institute of Engineering & Technology Department of CSE Uttar Pradesh Greater Noida201306 India School of Computing Science and Engineering Galgotias Univeristy Dept of Computer Application India

ISBN: (纸本)9798350327533

The world's most serious chronic liver condition is Non-Alcoholic Fatty Liver Disease (NAFLD). The early stage prediction saved people's lives. Recent machine and deep learning, help to predict and classify diseases. The main goal of this work is to make an advanced prediction system that uses plasma lipid data and MRI images to predict the stage-wise classification of NAFLD images. The four main stages of liver disease are normal, steatosis, steatohepatitis, and cirrhosis. The machine and deep learning techniques to identify and classify cirrhosis disease in NAFLD. This research study proposes a machine and deep learning techniques with features that classify and predict the NAFLD. The random forest algorithm is used to predict the disease images into binary classifications, and a deep neural network is used for stage-wise classification. Initially, different features are used to classify the liver. This work uses 52 features, such as GLGCM and GLCM to predict and classify the fatty liver. The proposed method combined the initial prediction features using the random forest method and deep neural network to classify different stages of livers images. The experimental work is done with the help of MRI images and various iterations for evaluation. The experimental results were evaluated using three metrics, such as specificity, sensitivity, and accuracy. The predicted values of this methodology were 96.26%, 95.7%, and 97.6%. © 2024 IEEE.

关键词： Cirrhosis Hybrid Method Random Forest-Deep Neural Network Stage Wise Classification

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Unsupervised anomaly detection algorithm for sensor data streams based on GAT-GAN 6

Unsupervised anomaly detection algorithm for sensor data str...

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6th International Conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Su, Yuhang Ma, Jun Fan, Jinyu Chen, Bohang Qinghai Normal University School of Physics and Electronic Information Engineering Qinghai Xining810016 China The College of Computer Qinghai Normal University The Computer Technology and Application College Qinghai University Qinghai Xining810016 China Qinghai Normal University The College of Computer Qinghai Xining810016 China

ISBN: (纸本)9781510682962

Detecting data flow anomalies within Wireless Sensor Networks (WSNs) and reaching the server can promptly identify faults and issue maintenance alerts, thereby ensuring safe and reliable operation of the system. However existing methods can utilize single variables in data streams to perform most anomaly detection tasks, they often ignore the correlation between variables, resulting in reduced detection performance. Therefore, our approach constructs a generative adversarial network model based on Graph Attention Network (GAT) and employs a dual autoencoding structure in both the generator and discriminator to learn the latent representation of the entire time series. The results of ablation experiments and comparison experiments on two real datasets with baseline models show that GAT-GAN outperforms the former in detecting data anomalies. © 2024 SPIE.

关键词： Gallium nitride

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Sentiment Analysis Based on Machine-Learning Classifiers for Datasets 8th

Sentiment Analysis Based on Machine-Learning Classifiers for...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Ghorpade, Pradip P. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766772

The study of sentiment analysis in natural language processing (NLP) holds tremendous promise for extracting meaningful emotional cues from textual data as a part of its core capabilities. This paper conducts an extensive exploration of sentiment analysis techniques powered by machine-learning classifiers, tailored to accommodate a variety of datasets. As a critical tool for assessing public opinion and sentiment, this study guides you through the crucial steps of data acquisition, preprocessing, feature extraction, and classifier selection, which are integral to the assessment of public sentiment. The research examines machine-learning approaches to sentiment analysis using a wide range of datasets, including social media content, product reviews, news articles, and much more, to determine if they are adaptable and scalable. In this study, "a number of classification models are examined, including Naive Bayes, Support Vector Machines, Random Forests, and deep learning models, and how their performance varies depending on the type of data". As a result of this analysis, the paper emphasizes the importance of utilizing machine-learning classifiers to distill nuanced sentiments, enabling applications in a wide range of fields, from brand reputation management to political sentiment tracking. With impressive accuracy, precision, F-measure, and correctly classified instances, Naive Bayes demonstrates rapid learning capabilities, while One-R displays promise with 91% accuracy, 97% precision, and 92% F-measure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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