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An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control 4

An IOT-Based Soil Moisture Management System for Precision A...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Laha, Suprava Ranjan Pattanayak, Binod Kumar Pattnaik, Saumendra Mishra, Debashree Kumar Nayak, Debasish Swapnesh Dash, Bibhuti Bhusan Iter Siksha O Anusandhan University Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350300888

Managing soil moisture is crucial for optimizing crop growth and yield in agriculture. With the advancements in Internet of Things (IoT) technology, innovative soil moisture monitoring and control solutions have emerged. This paper presents a pioneering IoT -based soil moisture management system tailored for precision agriculture within Siksha 'O' Anusandhan (SOA) University. The system utilizes sensor nodes, wireless communication, and cloud computing to enable real-time monitoring and automated irrigation control, addressing the limitations of conventional manual approaches. Providing continuous, remote, and precise measurements of soil moisture levels revolutionizes how soil moisture is managed within the university's agricultural practices. Low-cost sensor nodes are strategically placed throughout the fields to collect data, which is wirelessly transmitted to a central hub for analysis and decision-making. A cloud-based platform processes the data, generating actionable insights and triggering automated irrigation when necessary. Extensive testing and validation demonstrate the system's reliability and ability to improve water use efficiency, surpassing traditional methods. Furthermore, the IoT architecture of the system offers scalability and flexibility, making it adaptable to different crop types and field conditions present within SOA University. This innovative research contributes to the field of precision agriculture by providing an efficient and tailored solution for soil moisture management within the university's agricultural practices. This IoT -based system enhances crop productivity by enabling real-time monitoring and automated irrigation control, promoting sustainable water resource utilization. Its implementation within SOA University fosters the integration of cutting-edge technology into agricultural practices, facilitating optimized crop growth and contributing to the university's commitment to environmental sustainability and resource managem

关键词： Soil moisture

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Integrating Embedded Systems with Natural Language Processing: Innovations and applications in Intelligent Systems 4

Integrating Embedded Systems with Natural Language Processin...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Rawat, Ruchira Al-Fatlawy, Ramy Riad Bansal, Saloni Sehgal, Archana Gupta, Priyanka Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

The amalgamation of Natural Language Processing (NLP) with Embedded Systems signifies a revolutionary progression in the creation of intelligent systems, permitting them to participate in interactions that are akin to those of humans. This study looks into the creative ways that NLP and embedded technology can be used in a variety of industries, such as healthcare, automotive, and smart homes. The paper illustrates how NLP enables embedded devices to comprehend, interpret, and react to human commands, improving user experience and operational efficiency, by looking at the underlying principles. The main obstacles to this integration are thoroughly examined, including resource shortages, the need for real-time processing, and environmental restrictions. The study emphasises how important it is to use low-power, compact NLP models that enable real-time language processing without sacrificing system efficiency. Additionally, the research discusses the fundamental approaches for improving these models' using methods like transfer learning, quantisation, and model reduction. In the end, this work seeks to shed light on the enormous potential of voice-enabled intelligence in embedded systems, opening the door to more interactive, intuitive, and adaptable technologies that can easily adjust to changing human needs. This study advances the continuous development of smart gadgets by bridging the linguistic and technological divide, increasing their usability and accessibility in daily life. © 2024 IEEE.

关键词： Hidden Markov models

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Swarm Intelligence-Based Clustering Algorithms for Wireless Sensor Networks

Swarm Intelligence-Based Clustering Algorithms for Wireless ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Shnain, Ammar Hameed Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This work studies the use of swarm intelligence-based clustering algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) Bee Algorithm (BA), and Firefly Algorithm (FA), to optimize Wireless Sensor Networks. (WSNs). It evaluated their performance by strict simulations on energy consumption level, network lifetime, and clustering quality. It is observed that ACO and PSO consistently showed better results with moderate energy consumption by achieving long network lifetimes as well as precise clustering. However, BA and FA have been competitive but had lower efficiency when looking at certain metrics. The comparative analysis highlights the need for algorithmic selection according to specific demands in WSN deployments. The study complements related research on the application of swarm intelligence in WSN by focusing on routing challenges, energy-aware protocols, and centralized clustering solutions. The results give useful information to practitioners who are looking for the best algorithms, especially in real WSN situations. © 2024 IEEE.

关键词： Wireless sensor networks

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Optimizing Energy Consumption in Smart Buildings through Reinforcement Learning-Based Demand Response Strategies

Optimizing Energy Consumption in Smart Buildings through Rei...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Al-Attabi, Kassem Kumar, Rakesh Boob, Nandini Shirish Anand, Sami Gupta, Priyanka Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research studies the feasibility of RL-based response strategies that can maximize energy consumption in a smart building. By the inclusion of recreational pondering and observational investigation, RL operators automatically calibrated HVAC setpoints lighting levels level machine usage to reduce energy costs without compromising human comfort. It is thus evident from the results that significant value energy used on savings, RL-based algorithms achieved an average cost reduction of 16% compared to pattern based techniques. And the reasonable limits of tenant consolation hold standard fluctuation in temperature at 0.4 °C and variances from normal lighting level up to ten percent are also allowed. The occurrence of imperative violation is also reduced as RL-based approaches exhibit a lower number in comparison to rule based control and passive planning methods. © 2024 IEEE.

关键词： Reinforcement learning

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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A Deep Dive into Existing Lip Reading Technologies

A Deep Dive into Existing Lip Reading Technologies

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Khekare, Ganesh Majumder, Goutam Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science and Engineering Vellore Institute of Technology Vellore India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Lip reading, or Visual Speech Recognition (VSR), can revolutionize communication for those with hearing loss and in noisy environments. The researchers are trying to create advanced VSR systems using Deep learning models to overcome challenges. But still, several difficulties are there such as different shapes of lips during the video and different lightning conditions that can make deciphering or visualization difficult. Deep learning with the help of Convolutional Neural Networks (CNNs) helps to identify the exact shape and movement of a lip from a video frame. The continuous flow of lip movements in the video is handled by Recurrent Neural Networks (RNNs) which help in identifying words. However, RNNs are not good enough to remember the historical data. This issue is resolved by using Long Short Term Memory (LSTM) in the proposed research work. LSTMs have an additional internal memory that helps to store the history information for a longer period which helps to predict lip movement accurately even in rapid speech. The GRID and LRW datasets are used to generate the results. Additionally, VSR could improve video conferencing, allowing for natural communication even with compromised microphone quality. The impact of VSR extends beyond human-to-human and human-computer interaction. Frameworks like Streamlit offer a glimpse into the future, enabling the development of user-friendly lip reading applications. Deep learning unlocks the power of lip reading, paving the way for a future where communication go beyond the limitations of sound. © 2024 IEEE.

关键词： Video conferencing

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Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

Predictive Maintenance of Power Grid Infrastructure using Lo...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Alsalami, Zaid Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply. © 2024 IEEE.

关键词： Long short-term memory

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Deep Learning Techniques for Human Resource Management Optimization

Deep Learning Techniques for Human Resource Management Optim...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Ranawat, Pratyaksha Kumar, Manish Karthik, A. Asha, V. Nagpal, Amandeep Shaik, Bazani Jecrc University Computer Science Engineering India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Computer Applications Bangalore India Lovely Professional University Phagwara India Ramachandra College Of Engineering Department Of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350366846

Because deep learning and machine learning (ML) techniques have the potential to totally change how firms manage their human capital, there has been a lot of interest in developing HRM procedures that use these techniques. Strong DSS (Decision Support System) technology must be incorporated into the HRM (Human Resource Management) profession in order to make judgements that are effective in today's competitive environment. This study focuses on the problem of prediction, decision making, prediction, and testing assistance in a HRM system. The paper discusses a creative decision support system for HR procedures. Machine learning and deep learning techniques have been offered as fundamental instruments for tracking various HR indicators in the systems developed and implemented analytical process. The description of the suggested methodology and a discussion of a outcomes from the experiments are included in the paper. © 2024 IEEE.

关键词： Decision making

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