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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Shanmugasundaram Hariharan Pogula Srivani Sreenivasulu Gogula N. Venkateswaran Vinay Kukreja Jose P Dept. of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of H&S CMR Technical Campus Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Jyothishmathi Institute of Technology & Science Nustulapur Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics.

关键词： Degradation data analysis Machine learning Soil Agriculture Real-time systems Water resources Faces Farming Resilience

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A Novel Stress Identification based on the Health Aspects of an Individual using Strategical Learning Methodology

A Novel Stress Identification based on the Health Aspects of...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： P.N. Periyasamy Rajesh Kumar. K P. Gururama Senthilvel N. Duraimutharasan Hannah Rose Esther T Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Department of Electronics and Communication Engineering Chendu College of Engineering Madurantakam Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology AMET University Kanathur Tamil Nadu Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai

The modern human experience increasingly includes high levels of stress. According to the World Health Organization, stress is a mental illness that can negatively affect physical health. The effects of stress and depression can affect anyone, anywhere. There is tension in everyone's life. Stress is a major indicator of emotional well-being. Every area of a person's life, including their emotions, thoughts, and behaviors, are impacted by stress. Analyzing physiological signs is a traditional way to detect stress, according to earlier study. These readings come from sensors that are worn by the person being monitored. Researchers have attempted to detect stress by studying physiological information with traditional machine learning methods. The accuracy of the outcomes varies widely, from around 50% to almost 90%. Traditional machine learning methods have the issue of requiring handcrafted features. Accuracy drops if attributes are recognized wrongly. This research study has created a hybrid deep learning model to detect staff stress. With the intention of enhancing life quality, this study showed how deep neural networks may be used to create reliable, continuous, and noninvasive techniques for stress detection and emotion categorization. As they enable ready libraries for stress prediction, these ready tools and languages make it straightforward to predict stress. data science approaches are efficient in processing the training datasets and can accurately and quickly predict the human stress.

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Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth

Enhancing Business Intelligence Through A Semi-Distributed B...

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Disruptive Technologies (ICDT), International Conference on

作者： Mullaikodi C Sharath Kumar Jagannathan Jayanthi. N Rakhi Singh Yesha Tomar B Jaison Department of ECE J. N. N Institute of Engineering Tiruvallur Tamilnadu India Data Science Institute Saint Peter's University New Jersery USA School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Faculty of Commerce and Management SGT University Gurugrm Haryana India School of Management Doon University Dehradun Uttarakhand India Department of Computer Science and Engineering RMK Engineering College Chennai Tamilnadu India

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting.

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Toward a comprehensive hypothesis of oxygen-evolution reaction in the presence of iron and gold

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Journal of Energy Chemistry 2024年第2期89卷 172-183,I0005页

作者： Negah Hashemi Jafar Hussain Shah Cejun Hu Subhajit Nandy Pavlo Aleshkevych Sumbal Farid Keun Hwa Chae Wei Xie Taifeng Liu Junhu Wang Mohammad Mahdi Najafpour Department of Chemistry Institute for Advanced Studies in Basic Sciences(IASBS)Zanjan 45137-66731Iran Center for Advanced Mössbauer Spectroscopy Mössbauer Effect Data CenterDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina Key Lab of Advanced Energy Materials Chemistry(Ministry of Education) Renewable Energy Conversion and Storage CenterCollege of ChemistryNankai UniversityTianjin 300071China Advanced Analysis Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Institute of Physics Polish Academy of SciencesWarsaw 02-668Poland National and Local Joint Engineering Research Center for Applied Technology of Hybrid Nanomaterials Henan UniversityKaifeng 475004HenanChina Center of Climate Change and Global Warming Institute for Advanced Studies in Basic Sciences(IASBS)Zanjan 45137-66731Iran Research Center for Basic Sciences&Modern Technologies(RBST) Institute for Advanced Studies in Basic Sciences(IASBS)Zanjan 45137-66731Iran

This study investigates the effects of Fe on the oxygen-evolution reaction(OER)in the presence of *** distinct areas of OER were identified:the first associated with Fe sites at low overpotential(~330 mV),and the second with Au sites at high overpotential(~870 mV).Various factors such as surface Fe concentration,electrochemical method,scan rate,potential range,concentration,method of adding K_(2)Fe O_(4),nature of Fe,and temperature were varied to observe diverse behaviors during OER for Fe O_(x)H_(y)/*** amounts of Fe ions had a significant impact on OER,reaching a saturation point where the activity did not increase *** electronic interaction between Fe and Au ions was indicated by X-ray photoelectron spectroscopy(XPS)and electron paramagnetic resonance(EPR)*** situ visible spectroscopy confirmed the formation of Fe O_(4)^(2-)during *** situ Mossbauer and surfaceenhanced Raman spectroscopy(SERS)analyses suggest the involvement of Fe-based species as intermediates during the rate-determining step of OER.A lattice OER mechanism based on Fe O_(x)H_(y)was proposed for operation at low *** functional theory(DFT)calculations revealed that Fe oxide,Fe-oxide clusters,and Fe doping on the Au foil exhibited different activities and stabilities during *** study provides insights into the interplay between Fe and Au in OER,advancing the understanding of OER mechanisms and offering implications for the design of efficient electrocatalytic systems.

关键词： DFT calculation Fe/Au electrocatalysis Mechanism Oxygen evolution Water oxidation

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Enhancing Cybersecurity in IoT-Enabled Healthcare Systems: Challenges and Future Directions

Enhancing Cybersecurity in IoT-Enabled Healthcare Systems: C...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： M. Husain Bathushaw S. Nagasundaram G. Puthilibai S Chitradevi Darshan. R Department of Computer Applications VELS institute of science technology and advanced studies Chennai India Department of Chemistry Sri Sairam Engineering College (Autonomous) Chennai Tamilnadu India Department of Chemistry DMI College of Engineering Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College(Autonomous) Chennai Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Even within the health care industry, the IoT has lowered patient outcomes via realism, analytical estimates, and data sharing. However, it brings great threats of cyber security in terms of patients and data privacy, data accuracy, and availability of such systems. These challenges include Architecture and device heterogeneity constraint, poor resource availability as well as IoT and its associated security threats. They also analyze existing security measures by providing insights for further research to strengthen the existing features of such systems. It stresses machine learning, blockchain, and zero-trust architecture as probable solutions to the existing security deficits.

关键词： Hands Industries Prevention and mitigation Medical services Computer architecture Machine learning Blockchains Internet of Things Stakeholders Stress

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Utilizing Artificial Intelligence for Efficient Resource Allocation and Logistics in Humanitarian Aid

Utilizing Artificial Intelligence for Efficient Resource All...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： B Rajalakshmi P. Aswini H Pal Thethi Himanshu Rai Goyal Jajimoggala Sravanthi Ali Abdulhussein Hameed Department of Computer Science N.H.C.E Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of CSE GRIET Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

It's possible that artificial intelligence (AI) will change the way crisis relief is managed and delivered by providing flexible solutions that use real-time data insights and adjust to new situations. AI programs, which use a lot of data analysis, demand forecasts, and predictive models, can help emergency response systems change how they distribute resources. These projects' main goals are to provide quick and effective assistance while also making the best use of resources and reducing response times. Machine learning algorithms are important for improving decision-making because they allow systems to handle unexpected situations well and adapt quickly and correctly to changing times. The usefulness of these methods depends on their ability to handle transportation issues, which speeds up and improves the supply of aid while cutting down on waste and inefficient operations. Wanting to make the most of AI-driven solutions stresses the importance of being creative and giving when using them. These solutions have the potential to change the way businesses and charities handle their resources. Using AI to help plan crisis aid ensures that vulnerable people get the right help when needed and makes the best use of available resources. To find and use successful strategies that can clearly lower pain and save lives, it is important to understand the promise of artificial intelligence in crisis aid.

关键词： Decision making Supply chains Transportation Reinforcement learning Real-time systems Time factors Resource management Artificial intelligence Predictive analytics Genetic algorithms

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Artificial Intelligence enabled Employee Performance Prediction using Comprehensive Learning Metrics

Artificial Intelligence enabled Employee Performance Predict...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. GururamaSenthilvel N. Duraimutharasan Hannah Rose Esther T P. N. Periyasamy Rajesh Kumar. K Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Information Technology AMET University Kanathur Tamil Nadu Department of Artificial Intelligence and Data Science Velammal Engineering College Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Tamil Nadu Department of Electronics and Communication Engineering Chendu College of Engineering Madurantakam Tamil Nadu India

Human capital is crucial to the success of businesses in the new knowledge economy. There is a compelling financial justification for developing a system to assess and enhance employee performance, making performance measurement at the level of the individual worker a must. With data Mining, HR professionals may find new and relevant information (HRM). The success of an employee is not dependent on a single trait. Humans vary greatly in their innate talents and the traits that define their personalities. Hence, data from many sectors must be collected for performance monitoring. The purpose of this article is to present an overall grade of an employee's performance taking into account a number of criteria. We begin by contrasting many popular Machine Learning algorithmsagainst one another. We then recommend an ensemble method that draws from the aforementioned methods. The empirical findings show that the suggested model outperforms the state-of-the-art models in terms of precision, recall, F1-score, and accuracy, as compared to the more conventional models.

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Internet searches for “insomnia” and “suicide” mediated by stay-at-home behaviors in 45 countries during the first 12 months of the COVID-19 pandemic

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Journal of Affective Disorders 2023年第1期325卷 119-126页

作者： Lin, Sheng-Hsuan Lan, Yu-Tung Hsia, Pei-Hsuan Kao, Chu-Lan Michael Tsou, Hsiao-Hui Lin, Yu-Hsuan Institute of Statistics National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Psychiatry and Behavioral Science University of North Dakota School of Medicine and Health Sciences Fargo ND United States Institute of Population Health Sciences National Health Research Institutes Miaoli County Zhunan Taiwan Department of Psychiatry National Taiwan University Hospital Taipei Taiwan Department of Psychiatry College of Medicine National Taiwan University Taipei Taiwan Institute of Health Behaviors and Community Sciences College of Public Health National Taiwan University Taipei Taiwan

Background: This study aimed to investigate (1) the mental health impacts (i.e., insomnia and suicide ideas) of the COVID-19 pandemic and (2) the mediation effects of stay-at-home levels on those impacts. Methods: This study investigated monthly national COVID-19 deaths, stay-at-home levels, and internet searches for words for “insomnia” and “suicide” across 45 countries during the first year of the COVID-19 pandemic (March 1, 2020, to February 28, 2021). We used the changes of internet search volumes for “insomnia” and “suicide” (from the Google Trends database) to represent the mental health impacts, and the time of cell phone activity at the residence (from Google Location History) to estimate the stay-at-home effects. We computed the proportion mediated (PM) caused by stay-at-home levels in the COVID-19 impacts on insomnia and suicide ideas, respectively. Results: Throughout the first year of the COVID-19 pandemic, national COVID-19 deaths significantly correlated to increased internet searches for “insomnia” but decreased searches for “suicide”. In addition, the mediation effect was significant in the first six-month of COVID-19-related increases in insomnia (PM = 42.6 %, p = 0.016), but this effect was not significant (PM = 13.1 %, p = 0.270) in the second six-month. By contrast, the mediation effect was not significant in the first six-month of COVID-19-related decrease in suicide ideation (PM = 8.1 %, p = 0.180), but this effect was significant (PM = 39.6 %, p = 0.014) in the second six-month. Conclusions: Stay-at-home levels significantly mediated both increased insomnia and decreased suicide ideas, but within different time frames. © 2022

关键词： COVID-19 Google Trends Insomnia Mechanism investigation Mediation analysis Suicide

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Ensemble CNN Architecture with Multi-Modal Disease Detection for Sustainable Banana Crop Management: An Integrated Mobile Framework

Ensemble CNN Architecture with Multi-Modal Disease Detection...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： T.Satheesh Abarna A Pranesh N Queen Mary A Sindura B R.Dhivya Praba Department of Artificial Intelligence and Data Science Dr. N. G. P. Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The inefficiency of manual detection methods makes diagnosing banana crop diseases a serious threat to the agricultural sector. In this paper, we introduce ensemble deep learning of ResNet50, InceptionV3, and LeNet for complete diagnosis of the six most revalent banana leaf diseases. Our hybrid solution combines the mobile ease-of-use built with Flutter and TensorFlow Lite, allowing offline disease diagnosis with instant treatments for organic remedies. Our field testing showed remarkable reduction in diagnosis time compared to the traditional methods, and enhances sustainable farming through green treatment alternatives. The framework builds a new model for farm disease management that is simple, cost-efficient, and eco-friendly, and especially helpful to farmers in areas with poor internet access and low means.

关键词： Deep learning Accuracy Crops Telecommunication computing Sustainable development Artificial intelligence Diseases Residual neural networks Farming Testing

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Corrigendum to "A federated learning approach for classifying chest diseases from chest X-ray images" [Biomed. Signal Process. Control 100(Part A) (2025) 107107] (Biomedical Signal Processing and Control (2025) 100(PA), (S1746809424011650), (10.1016/***.2024.107107))

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Biomedical Signal Processing and Control 2025年 106卷

作者： Satapathy, Sandeep Kumar Cho, Sung-Bae Mishra, Shruti Sah, Sweeti Kamaldeep Mohanty, Sachi Nandan Dinesh, J. Department of Computer Science Yonsei University 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Engineering National Institute of Technology Haryana Kurukshetra136119 India Department of Media Engineering National Institute of Technical Teachers Training and Research Chandigarh160019 India VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The authors regret the omission of Mr. Dinesh J, School of Computer science and engineering, Vellore institute of Technology, Chennai, who initially undertook this work as a capstone project under the supervision of Dr. Sandeep Kumar Satapathy. His contributions in the early stages were instrumental in shaping the direction of this study. We sincerely acknowledge his efforts and appreciate his role in the initial development of this work. The correct order of authors list have been updated as above. The authors would like to apologise for any inconvenience caused. © 2024 Elsevier Ltd

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