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Smart irrigation for soil moisture prediction using hybrid AI and fog computing 14

Smart irrigation for soil moisture prediction using hybrid A...

引用

14th IEEE international conference on communication Systems and Network technologies, CSNT 2025

作者： Bindal, Ruchika Kaur, Mandeep Tandon, Righa Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331531935

Irrigation control is essential for optimizing water usage in smart agricultural systems, especially where there is water scarcity. The fast development of IoT and fog computing brings up new opportunities for developing new and advanced systems that are both efficient and flexible. Further integration of fog computing with machine learning will bring more developments to smart agriculture systems. This paper has framed an irrigation supervision algorithm to predict irrigation needs in the smart agriculture system. The methodology uses a hybrid algorithm that incorporates Gradient Boosting (GB), Logistic Regression (LR) and Random Forest (RF). The system accurately forecasts the irrigation requirement by processing ecological information such as humidity, temperature, and time-based factors. 97.78% of accuracy illustrates the reliability of the resultant model, and initial processing of the data illustrates that information is concise and accurate. Recall, precision and F1- score are computed as metrics for the performance. The system is integrated into fog computing and machine learning to manage real-time data processing to achieve faster and more accurate predictions. © 2025 IEEE.

关键词： Random forests

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Distributed Event Triggered Secondary control of Islanded Microgrid System 5

Distributed Event Triggered Secondary Control of Islanded Mi...

引用

5th IEEE international conference on Advances in Electrical, computing, communication and Sustainable technologies, ICAECT 2025

作者： Rao, B.Chiranjeev Ralhan, Shimpy Department of Electrical Engineering Shri Shankaracharya Technical Campus Bhilai India Department of Electrical & Electronics Engineering Shri Shankaracharya Technical Campus Bhilai India

ISBN: (纸本)9798350356236

The demand for decentralized control techniques in DC microgrids has increased due to their ability to enhance scalability, reduce communication requirements, and improve overall system efficiency. This paper presents a distributed event-triggered secondary control strategy for DC microgrids, aiming to regulate voltage and manage power sharing in a cost-effective and scalable manner. By implementing event-triggered mechanisms, communication between distributed energy resources (DERs) is minimized, leading to reduced overhead while ensuring system stability. This article investigates an event-triggered mechanism that incorporates event detection and adaptive control strategies to maintain frequency, voltage, proportional current and powersharing stability, while avoiding unnecessary controller interactions. © 2025 IEEE.

关键词： Decentralized control

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MANAS: Integrating advanced AI for Personalized Mental Health Support, Crisis Intervention, and Medical Insights 3

MANAS: Integrating Advanced AI for Personalized Mental Healt...

引用

3rd international conference on Intelligent Data communication technologies and Internet of Things, IDCIoT 2025

作者： Gayathri, S. Pradeep, Mutyala Sri Siva Sai Lakshmi Vara Prasad, N. School Of Computing Sathyabama Institute Of Science and Technology India

ISBN: (纸本)9798331527549

MANAS (Mental Assistance Network for Alleviating Suffering) is an AI-based system designed to address the pressing challenges in mental health care, including limited access to personalized, timely support and crisis intervention. By leveraging advanced AI techniques such as personality analysis using the Big Five model and emotion recognition through NLP, MANAS offers personalized responses to users’ mental health concerns. It also detects traces of crisis and triggers appropriate interventions, including notifying relevant authorities. The system generates detailed medical reports to aid healthcare professionals in diagnosing and managing mental disorders more effectively. This research demonstrates MANAS's capability to bridge the gap in mental health care with innovative and practical AI-driven solutions. © 2025 IEEE.

关键词： Emotion Recognition

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Empowering communication: Emergency Hand Recognition for the Differently Abled 3

Empowering Communication: Emergency Hand Recognition for the...

引用

3rd international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Lavanya Rao, U.B. Rekha, P. Asuti, Manjunath G. Kumar, Sumit Nadaf, Mehak Bedre, Shashank K. Bnm Institute Of Technology Department Of Electronics and Communication Bangalore India Bnm Institute Of Technology Dept. of Ece Bangalore India Bnm Institute Of Technology Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331515911

The advancement of technology has significantly influenced the lives of differently-abled individuals, particularly in enhancing their communication and interaction capabilities. This article presents an innovative emergency hand recognition system designed to improve interaction and communication for individuals with disabilities. Utilizing advanced computer vision and machine learning techniques, the system identifies predefined hand gestures, facilitating quick and accurate emergency communication for those with speech or hearing impairments. By providing a dependable way for users to express urgent needs, this solution seeks to enhance the safety and independence of specially-abled individuals. The implementation of this technology not only empowers users in critical situations but also promotes greater inclusion in society, allowing for more effective interactions with caregivers and emergency responders. Thus, this work aims to bridge communication gaps and foster a more accessible environment for all. © 2025 IEEE.

关键词： Adversarial machine learning

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Mitigating Security Challenges in Edge computing: Attacks, Defence Strategies, and Algorithms 3

Mitigating Security Challenges in Edge Computing: Attacks, D...

引用

3rd international conference on Intelligent Data communication technologies and Internet of Things, IDCIoT 2025

作者： Matta, Venkata Durga Rao Maheswara Rao, V.V.R. Battula, Venkata Sai Jayasurya Addagatla, Mahesh Babu Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331527549

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies. © 2025 IEEE.

关键词： Cloud security

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Privacy and Security Concerns in Edge-Cloud Systems: An In-Depth Analysis 3

Privacy and Security Concerns in Edge-Cloud Systems: An In-D...

引用

3rd international conference on Intelligent Data communication technologies and Internet of Things, IDCIoT 2025

作者： Kaki, Samuel Suhas Kantareddygari, Jagadeswara Reddy Aishwarya, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-time analytics. These systems are highly vulnerable to significant security and privacy challenges, which include the following: data confidentiality and integrity in the process of transmission, secure protection of sensitive data stored on the edge against physical and remote attacks, and maintaining availability under dynamic network conditions. More challenges related to ensuring users' private nature through anonymizing data, obtaining consent for all processes involved, and compliance with specific regulations like GDPR and CCPA. This work incorporates more enhanced cryptographic methods in the transfer of data between heterogeneous systems and environment-based anomaly detection, advanced privacy-preserving analytics to avoid extraction of private data, adaptive control mechanisms in place for Edge-Cloud for minimizing vulnerability with enhanced trust along with the maintenance of data integrity. The key benefits would be robust mitigation of threats, better interoperability between edge and cloud platforms, and alignment with evolving security standards, which promotes wider adoption across various sectors. © 2025 IEEE.

关键词： Cloud platforms

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A Comprehensive Survey on Unmanned Aerial Vehicles (UAVs): Types, Structural Components, communication Systems, and Operating Platforms 3

A Comprehensive Survey on Unmanned Aerial Vehicles (UAVs): T...

引用

3rd international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Tauseef, Md. Reddy, T. Sai Sankalp Lohith, R. Thanushree, M.P. Sravani, K. Reva University School of Ece Bengaluru India

ISBN: (纸本)9798331515911

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced rapid growth and widespread adoption across military and civilian applications in recent years. This paper offers a comprehensive survey of UAV technologies, focusing on their classifications, structural components, communication systems, and operating platforms. UAVs are categorized based on size, range, and altitude capabilities. Key structural elements such as frames, propulsion systems, sensors, and control mechanisms are examined. The paper also addresses communication requirements, architectures, protocols, and associated challenges. Furthermore, it reviews real time operating systems utilized in UAVs, high-lighting their design considerations. The survey concludes by identifying emerging trends and future research directions in UAV development, providing valuable insights for both newcomers and seasoned researchers in the field. © 2025 IEEE.

关键词： Drones

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Cloud computing in Enhancing the Efficiency and Integration of Alternative Energy Systems 2

Cloud Computing in Enhancing the Efficiency and Integration ...

引用

2nd international conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Sahoo, Amiya Kumar Prusty, Sucharita Jayasingh, Suvendra Kumar Swain, Asish Kumar Mohanta, Hemanta Kumar Aryan Institute of Engineering and Technology Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Odisha Cuttack India

ISBN: (纸本)9798331530389

In response to growing environmental concerns and the need for sustainable development, alternative energy systems have emerged as crucial solutions to the problems associated with fossil fuels. This research focuses on the significant role cloud computing plays in improving the efficiency and integration of various alternative energy systems such as solar, wind, hydropower, biomass, geothermal, and tidal/wave energy. The study explores how cloud computing technologies enable real-time data processing, storage, and analysis of the large datasets generated by these renewable energy sources, thus optimizing their performance and integration into the energy grid. Cloud computing provides advanced data management tools that are vital for managing the variable and intermittent nature of renewable energy production. By utilizing cloud-based platforms, energy producers can achieve improved forecasting, load balancing, and energy distribution, which are essential for enhancing efficiency and reliability. Additionally, this research highlights how cloud computing fosters collaboration and innovation by offering a scalable and flexible infrastructure. This infrastructure supports the development of new technologies and applications within the renewable energy sector, promoting continuous advancements and breakthroughs. This research underscores the crucial role of cloud computing in advancing a sustainable energy future. © 2025 IEEE.

关键词： Cloud platforms

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Adaptive Resource Allocation Framework with advanced technologies for Enhanced 5G Mobile Network Performances 2

Adaptive Resource Allocation Framework with Advanced Technol...

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Meghamala, Y. Paul, Pulipati John Kumar, Madam Aravind Dept of Electronics and communication Engineering Andhra Pradesh India Ellenki College of Engineering and Technology Telangana Patancheru India West Godavari Institute of Science and Engineering East Godavari District Andhra Pradesh Prakashraopalem India

ISBN: (纸本)9798331505745

The 5G networks are capable of executing a wide range of applications. Some of these applications are improved mobile broadband, massive Machine Type communications (mMTC), and ultra-reliable low-latency communication (URLLC). In order to satisfy these requirements adequately, resource management is essential. This work develops an approach of dynamic resource allocation with an emphasis on the use of cutting-edge technologies like edge computing, network slicing, AI, and ML for 5G enhancement. The system ensures dynamic resource allocation strategies which are aimed at reducing the service latency, conserving energy and improving the throughput of the system. This is done by leveraging user requests, real-time traffic patterns, and service quality indicators. The reduction in end-to-end latency of 25%, a 30% increase in energy efficiency and improved diversity of resource application are some of the quantitative improvements on QoS metrics that were achieved. Through simulations, the benefits were demonstrated. The results of this study suggest that the growing complexity of today's communication networks could be addressed by processes that are adaptive to the dynamics of advanced technologies. © 2025 IEEE.

关键词： Resource allocation

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advanced Temperature Forecasting in Major Indian Cities: A Comparative Study of SARIMA, Prophet, and XGBoost Models 4

Advanced Temperature Forecasting in Major Indian Cities: A C...

引用

4th international conference on Power, control and computing technologies, ICPC2T 2025

作者： Mishra, Ashutosh D'Souza, Aaron Kumar, Jatoth Ravi Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (纸本)9798331530402

This paper presents an analysis of temperature forecasting models across major Indian cities: Mumbai, Kolkata, Delhi, and Chennai by three methodologies that are Seasonal Autoregressive Integrated Moving Average (SARIMA), Prophet, and XGBoost. By analyzing historical temperature data, we implement a methodology that includes data collection, pre-processing, model selection, training, and evaluation. To evaluate the stationarity of the dataset, the Augmented Dickey-Fuller test is used, and seasonal differencing is applied to achieve stationarity. Both seasonal and non-seasonal components are fitted in the SARIMA model. The Prophet model is also fitted in handling missing data and trends, and XGBoost through machine learning techniques. The performance of each of the three models is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The forecasts are generated for six years and this study contributes to climate science by a comprehensive evaluation of temperature forecasting methodologies, that could be helpful in urban planning and policy-making. © 2025 IEEE.

关键词： Sorting

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