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Designing an Automated Smart Green House and Controlling Based on IOT-Enabled Intelligent Decision Support System

Designing an Automated Smart Green House and Controlling Bas...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： M Thomas K Vinayagam S Esakkiammal S Subramanian M Kavitha A Ganesa Murthy Department of Chemistry St Mother Theresa Engineering College Thoothukudi India Department of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Institute of Management Nirma University Gandhinagar India Department of Marritine Commerce Academy of Maritime Education and Training (AMET) Chennai India Department of Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Library Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In the agricultural field, optimization of land area and manpower is the challenging task of all farmers. The cost and time to spend for creating the farming with all necessary natural resources are not done properly due to their lacking knowledge and communication in the agriculture field. Problem Statement: Most of the farmers are followed the traditional agricultural methods to do crop cultivation and only a few of them are using the latest techniques. IoT is the framework used to provide automated control over agricultural land and monitoring using sensors on the plants. Perhaps, sensors are generated whole plant data then sent to the centralized controller for analytics to make decision making by the farmers. It takes a long time while plant growth is affected in the case of short-term plant cultivation. Methodology: To overcome this issue Smart Green House concept is introduced in the agriculture field for the improvement of plant growth within a stipulated time. It is possible to enhance the quality of the system with an IoT-enabled framework and controlled by a Smart Green House Intelligence Decision Support System (SGHIDSS). The system is working with several sensors placed on a greenhouse in various places and takes the decisions of actions like water supply, nutrition level automatically. All the sensors and control systems are working in an automated environment which leads the greenhouse technique to get successful for small farmers. Findings: This research work provides the framework or model for small-scale farmers and individuals to implement the smart greenhouse in their areas easily. Also, it provides less manpower and a small amount of natural resources for developing agriculture.

关键词： Decision support systems Natural resources Plants (biology) Green products Agriculture Sensor systems Software Servers Intelligent sensors Farming

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Detection Analysis of Abnormality in Kidney using Deep Learning Techniques and its Optimization

Detection Analysis of Abnormality in Kidney using Deep Learn...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Vemu Santhi Sri G.R Jothi Lakshmi Dept. of Electronics and Communication Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

Chronic kidney disease (CKD) is a global health burden that affects approximately 10 % of the adult populationin the world. It is also recognized as the top 20 causes of death worldwide. Unfortunately, there is no cure for CKD houserver, it is possible to slow down its progression and mollify the damage by early diagnosis of the disease. Therefore, the use of modern computer-aided methods is necessary to aid the traditional CKD diagnosis system to be more efficient and accurate. Our proposed model is RCNN to classify the Tumour Area in the X-ray Kidney Image. Compare the Deep Learning Techniques of Mask RCNN Model with other models. Evaluated model is compared with other models by metrics of the Mask R-CNN model and Tuned Hyper parameter CNN Model. It gives Training accuracy of 0.9861 and testing accuracy of 0.9389 in the 5th Epochs of Mask RCNN Algorithm. And also, method uses More Metrics of PrecisionRecall, and F1-Score by comparing the RCNN Model and Hyper tuned CNN Model.

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Analysis of titanium coated silencer exhaust gas emission in automobile vehicle using artificial neural network

Analysis of titanium coated silencer exhaust gas emission in...

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作者： Vijayaraghavan, P. Janakiraman, V. Surulivel rajan, T. Logesh, K. Rajiv Gandhi, N. Gnanavel, C. Sathi, G. Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

In developing countries like India, pollution is a major environmental problem. Among various factors of pollution, vehicular pollutions contribute major part in urban areas. The National Environmental engineering Research institute has estimated that about 60–70% of total air pollution is caused by automobiles in all major *** a perfect vehicular emissions model, Real world driving characteristics have to be analyzed. It is necessary to investigate the relationship between various parameters such as speed, age, power and driving behaviour of the vehicle. Vehicles particularly emit gases such as CO, CO2, HC, NOX, and O2. They are measured with five gas analyzer to find out the emission rates of four stroke two wheeler vehicles under static and dynamic condition. The factors considered are the age, power of the vehicle and driving behaviour. From the emission rates a prediction model is developed using artificial neural network and the composition of air pollutants will be calculated and then compared with emission standards. © 2022

关键词： Developing countries

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Deep Learning Technique to Detect and Diagnose the Anomalous in Kidney

Deep Learning Technique to Detect and Diagnose the Anomalous...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Vemu Santhi Sri G. R. Jothi Lakshmi Dept. of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

Several diseases that have had a impact on our society is kidney abnormalities, which rank as such ninth highest prevalent malignancy both in men and women globally. Premature KT identification has substantial advantages in terms of decreasing mortality rates, generating precautionary measures that lessen consequences, and eradicating the tumor. Deep learning (DL) automatic recognition techniques can speed up diagnostic, increase test precision, lower performance expenditures, and lighten the stress of physicians as contrasted to the laborious and time-consuming conventional diagnostic testing. This study established a deep ensemble model for identifying and categorizing different types of kidney ailments, including cyst, stone, tumor, and normal using computer tomography photographs. In addition, while executing layers in a neural network, the testing accuracy of the deep based pre-trained model reached a higher accuracy of 97.8% with 0.1 ms loss. With hybrid and other algorithms without normalizing pictures, several studies looked at how well 95.7% of kidney problems could be identified. However, the primary goal of our effort was to normalize the pictures in order to greatly increase computing efficiency and convergence speed.

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Performance Analysis of Phase Shift Full Bridge DC to DC Converter for Electric Vehicle using Flying Squirrel Search Optimization Technique

Performance Analysis of Phase Shift Full Bridge DC to DC Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sushita. K N. Shanmugasundaram Department of Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu

The current-voltage forecast of a photovoltaic system is linear and reliant on environmental elements like solar radiation and associated heat. The maximum energy point tracking (MPPT) method is used to determine the solar array's energy production to charge the Electric Vehicle (EV). This work proposes the design of a Photovoltaic power fed Phase Shift Full Bridge (PSFB) DC to DC converter with Flying Squirrel Search Optimization (FSSO) Technique. The MPPT control algorithm creates switching signals which are fed to the converter, i.e., duty cycle. The control algorithm (FSSO) is compared with other Metaheuristic algorithms such as the Salp Swarm Optimization Algorithm (SSO), Emperor Penguin Optimization (EPO). Once the converter achieves the desired power, the rest of the power from the Photovoltaic source is used to charge the off-board battery. The Matlab/Simulink is used to develop the proposed system and the overall efficiency of each technique is compared.

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Study on Multi-Viral Infection on Lungs using Data and Predictive Analysis Techniques

Study on Multi-Viral Infection on Lungs using Data and Predi...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sudha Varalakshmi P Vijayalakshmi. V. Rajendran Department of Electronics and Communication Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

This research study has used deep learning techniques (DL) to classify spectrograms of acoustic signals. In addition, this study intends to differentiate spectrograms that contain a cough and those that do not contain a cough, and once it is known that a spectrogram contains a cough, it is possible to understand the underlying disease. The main goal is to obtain a system with better performance than those proposed so far in the literature in the field of cough detection and make a first approximation to the classification of diseases based on audio clips with cough.

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Maximum Energy Transfer Efficiency in Solar-Powered Bilateral Dual-Stage AC to DC Converters for Vehicle-to-Home and Vehicle-to-Grid Applications

Maximum Energy Transfer Efficiency in Solar-Powered Bilatera...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： S. Jaanaa Rubavathy M. Bharathiraja K. Sasikala Gomathi S Saveetha School of Engineering SIMATS Saveetha University Chennai Tamilnadu India Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Transition to alternative sources of energy is essential to halting global warming and lessening its effects on biodiversity. In this research, the beneficial environmental effects of integrating powered by solar energy multiple stage bidirectional AC to DC converters in Vehicle-to-Home (V2H) and Vehicle-to-Grid (V2G) systems are investigated. By enabling efficient energy transmission between electric vehicles (EVs), the grid, and household energy systems, these converters enhance the handling of energy and long-term sustainability. Due to its simple construction and customizable properties, 2 stages isolated AC/DC converters are frequently employed in the rapidly expanding renewable energy vehicle industry. Here, we construct and assess a highly effective dual-stage in-car charger that operates effectively across a broad voltage range for both I/P and O/P. The main portion comprises PFC (power factor correction) converters with bridgeless buck-boost completely soft switching design, while the battery end converter is powered by an isolated filter-in-built direct current converter. The primary stage’s principal benefit is the elimination of electromagnetic interference (EMI) caused by sudden voltage fluctuations by creating an I/p and O/P common ground. This lessens the requirement for EMI filtering in common mode. Both the common mode (CM) noise and the total leakage current are maintained at a reasonable level. Greater efficiency and power density are the main advantages of the second stage. In order to make sure the safe charging and discharging of batteries, a converter that offers controls battery current and galvanic isolation is needed.

关键词： Vehicle-to-grid Electromagnetic interference Soft switching Solar energy Power factor correction Zero voltage switching Batteries Automobiles Sustainable development Zero current switching

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Behaviour of Lignosulphonate Amended Expansive Soil 1st

Behaviour of Lignosulphonate Amended Expansive Soil

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1st International Conference on Sustainable Practices and Innovations in Civil engineering, SPICE 2019

作者： Landlin, G. Soundarya, M.K. Bhuvaneshwari, S. Department of Civil Engineering SRM Institute of Science and Technology Chennai India Department of Civil Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9789811551000

Expansive soils are problematic soils which exhibit large volume change behaviour on exposure to moisture changes. These soils are often chemically treated to mitigate the volume change behaviour. The chemical treatment involves the addition of chemical additives and various other waste materials which can curtail the volume change behaviour. However, the usage of chemical additives leads to large environmental issues and affects the biodiversity of the surrounding environment. In an attempt to cater to these issues, a non-conventional additive, with a bio-polymer base namely Lignosulphonate is used for treating the expansive soil. The soil selected is of highly expansive in nature, and the additive is added in small percentages of 1, 1.5 and 3%, and its effects on various soil properties are evaluated. The bio-polymer based additive basically curtails swelling and has a good influence on strength and improvement of compaction characteristics. The microstructural aspects are also evaluated through XRD (X-Ray Diffraction) and SEM (Scanning Electron Microscope) techniques, in order to understand the changes at the microstructure level, particle orientation and probable formation of cementitious compounds, and the comprehensive behaviour of the additive amended expansive soil. The lignosulphonate-treated samples depicted a 5-fold increase in strength, due to the particle aggregation and floc formation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Microstructure

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Parametric Appraisal of Mahua Biodiesel-Powered Compression Ignition Engine to Improve Performance Characteristics by RSM Coupled WASPAS Simulated Annealing and Genetic Algorithm 3rd

Parametric Appraisal of Mahua Biodiesel-Powered Compression ...

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3rd International Conference on Recent Advances in Mechanical engineering Research and Development, ICRAMERD 2022

作者： Sahoo, Bibhuti Bhusan Jena, Shakti Prakash Barua, Abhishek Jeet, Siddharth Kumari, Kanchan Bagal, Dilip Kumar Saran, Madhumanjari Odisha Bhubaneswar India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Odisha Rourkela India Department of Mechanical Engineering Parala Maharaja Engineering College Odisha Berhampur India Department of Mechanical Engineering Government College of Engineering Odisha Kalahandi Bhawanipatna India Department of Mechanical Engineering Indira Gandhi Institute of Technology Odisha Sarang Dhekanal India

ISBN: (纸本)9789811994920

This experimental study represents the determination of optimum operating parameters of diesel engines powered by different low-percentage Mahua biodiesel-diesel blends for the improvement of performance characteristics. Different mixes of Mahua biodiesel-diesel, engine loading, and fuel injection pressure were considered as input operating restrictions with varying degrees of severity, and severity was determined. The Box-Behnken approach, which is centred on Response Surface Methodology, was utilised for experimental design, and for evaluation of brake thermal efficiency and exhaust gas temperature, along with the brake-specific fuel consumption. The usage of the weighted aggregated sum product assessment technique in conjunction with the Simulated Annealing Algorithm and the Genetic Algorithm was to optimise engine-operating parameters in a hybrid manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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