ABSTRACT: Cyber Physical System is showing a very important part in the arena of accuracy agriculture and it is predictable to progress productivity in order to provide food to all and prevent hunger. To accelerate the consciousness of CPS in the arena of accuracy agriculture it is essential to grow some devices, software tools and hardware components depend upon interdisciplinary methods, along with authentication of the ideologies through prototype. In this framework this paper offerings a precision agricultural supervision model through drone based on CPS design technology. Accuracy agriculture means to include the cleverness in the production of the harvest, by real-time identifying technique, optimization for improving health of soil-crop, as well as most efficient cyber-occupied tools for computerization and adeptness. Combined analysis of agricultural and ecosystem of soil is in promising stages, but increasing progressively with enhancements in sensing skills and data-influenced decision making. Our target to grow an adaptive Sensor based -Drone-Satellite system for endorsing agricultural operations and sustainability through matching often-conflicting purposes (e.g. price ecological or environmental and commercial). This integrated system is a theoretical prototype constructed on a cyber-physical interface to enable real multi-scale manual decision making by coupling innumerable existing and yet-to-be implemented data resources with AI techniques (e.g. artificial neural networks). The planned flexible system search for best solutions that can efficiently support fundamental, improve agriculture reliability and finally food safety.

ABSTRACT: In the present world, online social networks are rich in multimodal data sources with various objects, URLs, and comments. These are the real-time dynamic sources for the analysis which will lead to the discovery of facts and hidden relationships among the closed community groups in networks. Finding a closed community in online social networks is a challenging task for various purposes of applications. A closed community is formed with a group of similar-minded people and may be related to political, ethnic, or religious. The governance of such groups consciously applies limitations on the network links with outside communities. Broadly, two concepts of algorithms viz., clustering and network partitioning are used for the detection of such groups. These algorithms are based on dynamic networks with humans as key players and the other one is the graph structure similar to the topological structure. However, these algorithms suffer from limitations such as these communities provide no knowledge of groups in advance, the requirement of an extensive analysis of all possible partitions, etc. This article aims to overcome the said limitations by using the fast greedy approach by fusing with a Density-based clustering technique called DBSCAN for the detection of such communities. Detection and deletion of these noisy nodes in the communities lead to the development of quality. The comparison of the experimental results proved that the removal of noisy nodes will impact the quality of the community detection.

ABSTRACT: In recent times, the population of Blind people has been increasing by around 250 million in the world, and an attempt to improve the quality of their lives. However, despite the promising research outcomes, the existing wearable aids for blind or visually impaired people have numerous weaknesses in terms of weight, feature limitations, and cost. In this manuscript, a novel invention of lightweight design of wearable aid for visually impaired and blind people. The proposed design of a wearable aid will help them to walk and detect the environment around them with ease and make them independent in their life. The proposed system uses the fusion of sensor and vision-based technologies. It includes Google Vision API services, Lidar Technology, Arduino Nano, and Raspberry Pi 4. Based on the appearance of this prototype, this invention is named Blind's Bib. This assures the proposed aid is designed with lightweight, easy to use, and with a minimum number of instructions for operation. All the necessary security and frequently needed features are included in this system. Experimental results are demonstrated with blindfolded subjects and visually impaired participants.

ABSTRACT: This paper reviews the various control strategies used for Dc-Dc boost converter applied with renewable energy systems, like photovoltaic array module and wind energy system. Boost converters are in prime position for lifting up the lower voltage level collected from renewable energy sources (RES), such as fuel cells and solar panel, to cope up the required level of the grid voltage. Useful control techniques are required for assuring circuit efficiency, stability and reliability under different condition. This paper classifies control methods into conventional method and advanced methods. Each method’s benefits, drawbacks and suitability for dynamic and unbalanced renewable energy supply. Distinct concentration is provided for real-time adaptability, fast response and minimized power losses vital for improving renewable energy system applications.

ABSTRACT: Characterizing respiratory reactions to external stimuli is essential for understanding the complicated respiratory system, especially in clinical diagnosis and treatment. This research uses model-based system identification to correctly record and assess the respiratory system's reaction to mechanical ventilation, environmental changes, and pharmaceutical drugs. We use advanced system identification methods to create a dynamic model that accurately captures the respiratory system's nonlinear and time-varying behavior. Data-driven modelling and physiological insights are used to determine respiratory function parameters in the proposed strategy. The model is validated using controlled laboratory and clinical trial data. The results show that the model-based method accurately predicts respiratory responses, improving respiratory dynamics comprehension and management in healthy people and respiratory problem patients. This study advances respiratory physiology and biomedical engineering by improving respiratory function monitoring, prediction, and optimization in response to external stimuli. The system identification framework may improve patient-specific treatment techniques and individualized respiratory medicines.

ABSTRACT: Internet of Things (IoT) is an Industry 4.0 Technology that is rapidly getting implemented in the industries and is replacing the conventional and obsolete parts of the machines. In a transformer, the rise in temperature of the winding and the transformer oil, which is the most common coolant, beyond a certain limit signifies that the insulation may be damaged. Conventionally, the protection scheme implied is the Oil Temperature Indicator (OTI) and the Winding Temperature Indicator (WTI). This work uses IoT to transmit and deploy alarms based on wireless and cloud technologies which significantly increased the efficiency of the protection scheme. The hardware model developed attempts to incorporate the benefits of the IoT and deploy the alarm message and alarm sound in significantly lesser amount of time and enable the required safety measures to be taken as soon as possible.

ABSTRACT: The Battery Management System (BMS) is crucial for the functioning of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs), ensuring the safe and reliable functioning of the battery. The primary objective of the BMS is to monitor and regulate the state of the battery, ensuring optimal performance and longevity. The main functions of the BMS include monitoring and assessing the state of the battery, controlling charging, and balancing the cells. These features are essential for maintaining battery safety and efficiency. Rechargeable batteries supply power to the motor and auxiliary systems in electric vehicles. Over the past decade, battery technology has made significant advancements, leading to the development of high-performance batteries. This paper focuses on the critical tasks handled by the BMS, including monitoring the State of Charge (SoC), State of Health (SoH), State of Life (SoL), and maximum capacity of the battery. By examining the various methodologies used to assess these parameters, the paper identifies future challenges and potential solutions to enhance battery management systems.

ABSTRACT: Statistical clustering technique can be used by crime analysts to the generate suspected list of unsolved crimes, locate crime clusters which have committed by the same person or group of persons, forecasting future events, & develop offender profiles. In this paper, the log-bayes-factor and maximum posterior probability has been used as a similarity measures for solving an unsolved case. Since the offender is known only for a fraction of crimes, in this article the proposed approach is partially semi-supervised. It employs the crime attributes, along with spatial and temporal locations, to describe the offender. It is possible to link and compare crimes using a single link, average link, and a complete link strategy. It employs the agglomerative hierarchal-based clustering method for making crime clusters, based on log Bayes factor and Bayesian clustering model which uses the maximum posterior probability as a similarity measure, for unsolved crime identification. Naive Bayes model calculates the Log-Bayes factor, which helps investigators uncover unsolved murders that are linked to one another. The Naïve Bayes classifier outperforms than the Agglomerative Hierarchical Clustering(AHC) as it uses the log Bayes factor.

ABSTRACT: An innovative method for improving auditory and sensory experiences is the incorporation of vibration-based music therapy into the rehabilitation process for those with hearing impairments. This study uses machine intelligence and advanced digital signal processing (DSP) techniques to assess the efficacy of this therapy. Through the analysis and processing of vibration-based music signals, the research endeavors to measure the influence of these signals on hearing-impaired individuals, emphasizing enhancements in sensory perception and general well-being. As part of the process, a DSP framework is created to precisely record and adjust vibrations that the hard of hearing experience. To provide individualized therapeutic experiences, machine intelligence algorithms are used to understand and modify these vibrations based on each person's unique sensory profile. The three main criteria that are measured are cognitive engagement, emotional well-being, and sensory responsiveness. According to preliminary findings, vibration-based music therapy has potential advantages in enhancing sensory integration and emotional reactions in hearing-impaired individuals when linked with advanced DSP and machine learning methods. This study adds to our knowledge of complementary and alternative therapies and emphasizes how digital technologies might support conventional rehabilitation techniques. The main goals of future study will be to improve these methods and apply them to larger groups of people.

ABSTRACT: Today’s electronic devices market depends on wide band semiconductors with the members of chalcogenide compounds. They are chemicals which contain group 16 elements led by oxygen and followed by anion elements, like selenium, sulfur, selenium, tellurium, and others. On the other hand, second components include cations, like, zinc, beryllium, copper, and others. At present, chalcogenide compounds are used for infrared optical windows with strong refractive index. They are used in different optical sensors, especially in photodiode, photo sensor, optical transmission. Obtainable in crystal-like, and nano-crystal-like forms, chalcogenides are noted for their superior electronic, optical, and semiconducting properties. We have discussed present utilization and prospects of this semiconductor. It has been observed that chalcogenides with Cu as another cation component are good to use due to their less toxicity. Even though it is in nanocrystal form. Other chalcogenides are comparatively toxic, for example, cadmium and lead. But they are effectively used in biomedical applications. On the other hand, some applications in photovoltaics are supplemented with copper chalcogenides. They are efficient in clean energy transformation as they efficiently work on photocatalytic activity. In this paper we have reviewed evolutionary applications of chalcogenide compounds in different domains.

ABSTRACT: With the advancement in automation and smart systems, many problems that occur due to human error and inefficiency has been reduced prominently in different fields. In the field of power system, smart metering is a revolution to overcome problems of human inefficiency and to create awareness for consumers about their energy usage. It is possible due to Internet of Things technology under industrial revolution 4.0. This paper addresses the various aspects of different wireless communication technologies utilized in the IoT integration of smart meters and analysed the performance of these technologies on the parameters which is essential for smart metering application. In the country like India, where most of the populations are living in the rural areas and the network connectivity is still a big challenge. The installation of smart meters in various household is still in progress and various communication technologies have been used for the integration in smart meters but still we are facing many issues which are discussed in this paper. Although there are numerous review papers on wireless communication technologies, none of them have specifically addressed how to incorporate these technologies into applications for smart meters. Based on the issues and challenges associated with existing smart meters, a detailed analysis of wireless communication technologies on multiple aspects which are essential for the integration of communication technologies are discussed in this paper which can be helpful for the researchers and companies working in this area. Since not a single technology is perfect in all parameters, we found LoRaWAN- a viable option for smart metering application.

ABSTRACT: Efficient use of renewable energy sources without limiting power consumption is the problem in demand-side energy management. This paper presents the design and implementation of an IoT-driven hybrid power management system that seamlessly integrates solar and AC power sources. Leveraging the ESP32 microcontroller, this system is engineered to prioritize solar energy as the primary power source, switching to AC only when solar output is insufficient or an overload condition occurs. This dynamic switching mechanism not only ensures a consistent power supply but also optimizes energy usage, contributing to both cost-efficiency and environmental sustainability. Proposed design allows users to track power levels, receive notifications on power source switching, and manage overload conditions remotely.
Keywords: Hybrid Power, IoT, ESP32, Solar Energy, Mobile Application, Renewable Energy .

ABSTRACT: By combining cutting-edge natural language processing methods with the AI-SHAP framework, this study offers a novel approach for identifying and evaluating toxic language in digital communication. The research not only improves the interpretability of hazardous text categorization models by utilising AI-SHAP, but it also explores the complex linguistic subtleties and contextual complexities that underlie toxic behaviour in online contexts. This method clarifies the fundamental causes of toxic communication through thorough assessments on a variety of datasets, opening the door for the creation of practical tactics to promote safer and more welcoming online communities. It makes use of a novel approach AI-SHAP framework to examine and identify harmful language used in online communications. It also uses advances natural language processing techniques for reliable text analysis.
Keywords: Toxic Language, Natural Language Processing, AI-SHAP Framework, Logistic Regression

ABSTRACT: India's agriculture industry has a difficult time cutting expenses without sacrificing crop production. This research suggests uses of Internet of Things (IoT) devices to propose a framework for cost optimization in agriculture. We introduced a multifaceted strategy that incorporates: Using cameras to monitor fields remotely and monitor crops in real time, Automated systems for discouraging animals with remote-controlled buzzers ,utilizing intelligent sensors and actuators to manage irrigation precisely With our IoT-based system, farmers can keep an eye on their fields from a distance, identify possible problems, and take preventative action to avoid damaging their crops. The technique minimizes agricultural losses from animal encroachment, maximizes crop utilization, and lowers labour expenses. Our pilot study's findings show a notable decrease in the expenses related to: Labour, Use of water and Crop loss. Our research indicates that the suggested IoT-enabled framework can significantly optimize agricultural expenses, improving their profitability and sustainability. By advancing smart agriculture techniques, this research opens a new door for upcoming advancements in resource management and cost optimization in farming.
Keywords-Smart-farming, Animal deterrence, Renewable energy, Precision Irrigation and Cost Optimization

ABSTRACT: Voltage sags, characterized by temporary drops in voltage levels, are among the most prevalent and disruptive power quality disturbances in modern electrical systems. These events, caused by factors such as short circuits, sudden load changes, and network faults, pose significant risks to sensitive equipment and industrial processes, leading to operational disruptions and financial losses. Ensuring stable voltage levels is critical for maintaining high power quality standards and uninterrupted operations in today's interconnected and digital environments. Dynamic Voltage Restorers (DVRs) have emerged as effective solutions for mitigating voltage sags by injecting compensatory voltage to stabilize the load-side supply. However, conventional DVR controllers, like proportional-integral (PI) and proportional-integral-derivative (PID) controllers, often face challenges in adapting to dynamic and complex sag conditions. To address these limitations, this study proposes a DVR system enhanced by a fuzzy logic controller (FLC). Fuzzy logic offers a rule-based, adaptive approach, enabling the DVR to respond effectively to varying sag scenarios and improve voltage regulation. Presented FLC is working in different voltage sag ranges in an effective manner. Proposed FLC is adequate, effective and efficient one as it provide required level of voltages by operating in 3 modes (Low compensation, medium compensation and high compensation). This ensure the electric power saving too and hence more efficient than other controller. The research investigates the performance of the FLC-enhanced DVR in mitigating voltage sags under diverse fault conditions.
Keywords: Power quality; Voltage sag; Short circuit fault; Dynamic voltage restorer; Sag mitigation; Fuzzy logic controller.

ABSTRACT: The increasing frequency of negative online behaviour has drawn a lot of interest to predictive analysis of cyberbullying on Twitter data. This research suggests a unique method for predicting incidents of cyberbullying on Twitter by using a multi-model supervised strategy. The proposed approach aims to enhance efficacy and enhance the precision of cyberbullying detection through the integration of textual, social, and network attributes. The models are trained and assessed using Twitter data sets that include both cyberbullying and non- cyberbullying events. Sentiment analysis, bag-of-words, and semantic similarity are examples of textual features; follower count and account age are examples of social features. Analysing the user's interaction patterns and network structure is part of network features. The models are created and assessed using a variety of machine learning algorithms, including support vector machines (SVM), random forests (RF), and neural networks (NN). The outcomes of the experiments show that the combined strategy outperforms the individual models in terms of predictive performance. The significance of feature selection in enhancing model accuracy is further emphasised by the study. This research helps establish practical tactics and countermeasures to lessen the negative impacts of cyberbullying by precisely detecting incidences of cyberbullying on Twitter.
Keywords: Support Vector Machines, Random Forests, neural networks, textual features, social features, network features, cyberbullying prediction, Twitter data, multi- model supervised approach, and feature selection.

ABSTRACT: Anticipating Blood Glucose (BG) levels for patients can assist in averting hypoglycaemia and hyperglycaemia episodes beforehand. Therefore, to predict Blood Glucose (BG) values in Prediction Limits (PHs) of 15, 15, 30, and 60 minutes, this study suggests a predictive for blood glucose management using a deep learning algorithm. The model proposed by the author uses past Blood Glucose values congregated by devices used for constant monitoring of glucose as an endogenic feature and information about insulin administration(times) and carbohydrate intake as an exogenic factor. In this study, authors developed a predictive model for Type-1 diabetes using clinical parameters. The four prediction models were taken, which are subjected to a Bat Algorithm (BA) to optimize the weights using ensemble approaches and provide the ultimate predicted BG values. The model performance was rigorously evaluated using RMSE and loss function metrics to assess the accuracy and convergence. RMSE value of 0.082 was achieved, indicating a high level of precision for blood glucose level prediction; while the loss function stabilized at 0.0068, demonstrating the model's effectiveness in learning from the dataset. These results surpass current benchmarks and suggest that further refinement, such a model could significantly enhance the predictive monitoring of Type-1 diabetes. The findings in the paper indicate that the suggested model performs noticeably better than the baseline after applying four different algorithms for further optimization of results.
Keywords: Deep Neural Network, Deep Learning, Ensemble Learning, Feature Selection, Prediction Model, Type-1 diabetes.

ABSTRACT: Energy management between electric vehicles and the grid system for hybrid integration is the primary goal of this Endeavour. Amidst ecological degradation and the depletion of fossil fuels, the global renewable energy generation scale, particularly for wind power, has grown significantly in recent years. However, when connected to the power grid, the randomness, intermittent nature, and uncertainty significantly impair the power system's dependability and lead to several issues. Therefore, in order to guarantee the safety and stability of the power system, grid-connected electricity must fall within specific bounds. Additionally, increasing the rate at which hybrid power generation is incorporated into the power system has become extremely. A method for mitigating the oscillations of large-scale wind power is examined through the utilization of vehicle-to-grid (V2G) systems. Battery cells are gradually being replaced by electric vehicles (EVs) as energy storage components in an effort to lower the investment costs of energy storage. Furthermore, as the number of EVs rises, its operability gets increasingly better. Therefore, an energy management and optimization system is created and modeled in order to overcome the voltage variations and manage the energy quickly in this approach. In this method,we are going to discuss about how the energy management takes place when the power is supplied from wind to grid system. Energy management plays a key role in the power system.So, we are going to consider mainly 3-cases based on the equal distribution of power to the connected loads. They are 1) connecting to wind, 2) connecting to solar and connecting to 3) Hybrid System. In the first case the energy management is obtained by considering the power from wind to grid system and the supply of power to loads, Electric Vehicles and to the consumer loads. In the second case the energy management is obtained by considering the power from solar to grid system and the supply of power to loads, Electric Vehicles and to the consumer loads. In the third case we will connect both solar and wind as a hybrid resources to grid system and the supply of power to loads, Electric Vehicles and to the consumer loads.
Keywords: Electric Vehicle(EV),Solar Photo Voltaic(SPV), Wind Turbine(WT), Hybrid Energy Resource(HER),Grid connected system, Matlab/Simulink.

ABSTRACT: In the current environment full of data, the demand for concise and clear summaries of lengthy articles and narratives has become increasingly critical. This document examines the application of the T5 model in generating efficient summaries, drawing upon previous studies conducted in this field. The objective is to enhance the performance of the T5 model by increasing its accuracy, targeting a level of approximately 97% accuracy. The T5 model serves as an advanced resource in the field of Natural Language Processing, facilitating the transformation of intricate data into clear and concise summaries. This study aims to enhance text summarization methodologies, particularly for extended texts, thereby improving content comprehensibility and accessibility. This approach effectively tackles the increasing demand for enhanced information processing capabilities in the digital era.
Keywords: Natural language processing; Text summarization; Abstractive summarization; Extractive summarization; Transformer; T5 Model

ABSTRACT: India is an agriculture-based country. Agriculture, with its allied sectors, is undoubted, the largest livelihood provider of this country, more so in the vast rural areas. Farmers work day and night without getting concerned about their health. So, we have come up with an idea of reducing their workload considerably by a robot that can plough the soil, spray water and pesticides, sow the seed and give fertilizer not only to agricultural fields but also to any kind of farming. According to us, this will be an efficient robot, both in terms of the production cost and health perspectives, and will be of immense help to a farmer, thus, will help in the development of the agricultural system of India.
Keywords: Robotic application; automation in agriculture; 4. Arduino UNO; 9. ESP32 Camera;

ABSTRACT: Vehicular Visible Light Communication (VVLC) presents a promising alternative to traditional radio frequency (RF) technologies in Intelligent Transport Systems (ITS) due to its immunity to electromagnetic interference and use of existing light-emitting diode (LED) infrastructure. This study investigates the performance of a Hadamard variance-based adaptive Normalized Least Mean Squares (NLMS) filter for noise cancellation in VVLC systems, comparing it to the previously established Allan variance-based filter. The analysis reveals that while Allan variance excels at low signal-to-noise ratios (SNRs) by efficiently handling white noise, Hadamard variance demonstrates superior performance at high SNRs by mitigating the impact of random walk noise and linear drifts more effectively. This study's results indicate that the Hadamard variance-based adaptive filter improves bit error rate (BER) performance significantly, especially in high SNR conditions, thus enhancing the reliability and efficiency of VVLC systems in varying noise environments.
Keywords: Intelligent Transport System (ITS), Visible Light Communication (VLC), solar interference, Allan variance, Hadamard variance

ABSTRACT: In this paper it has been demonstrated that IoT (Internet of Thing) based solution can be used to improve the safety and utility of home kitchen or Commercial kitchens. Work has been done towards finding an integrated solution for the problems (Fire, smoke, hazards, etc.) that are very common now-a- days and these may lead to accidents and even loss of life. Using various sensors and a IoT controller, the system has been designed for giving the control to the user’s mobile so that any kind of accidents can be stopped beforehand. The user will have access to the parameters (Temperature, smoke, sudden movement etc.) in real time. The user will be notified when parameters go out the safe limits. There will also be additions equipment that can be activated remotely in case of an accident to minimize the loss ensuring the safety. The controlling can be transferred based on the requirement and only authorized used it possible. The mobile and IoT connections is possible through Wi-Fi or internet at both ends. The user can control the operations using a app which will establish connection between the mobile and IoT through internet.
Keywords: Digital and Humidity Sensor, IR Sensor, MQ2 Sensor, Flame Sensor and IoT.

ABSTRACT: In our modern world of technologies our lifestyles have become very busy and restless, which also gives a simultaneous increase in usage of our portable electronic devices in our daily life. Just like our mobiles and laptops. But these devices consume a lot of energy and get drained up pretty easily because it has many apps and software which uses very much power [1] whilst using them, and in some situations, it puts us in a very hard situations where we might require a little bit of extra time to communicate and express your device situation. Therefore, it requires a source of power supply in that particular situation which can keep them going on for those extra few minutes. As we know, it is next to impossible to get a charging point anywhere and everywhere, and even we also seen people fighting and standing a queue to get access to the charging port. Nowadays, the concept of renewable energy in human surrounding instigates a renewed interest. The aim of our project is that we deal with a device which is using piezoelectric material [2] that provides an alternate means for powering mobile/portable devices. A piezoelectric material generates an electric charge while a mechanical stress is applied to it. The ambient mechanical energy can be transformed to vibration of electrical energy by using the mechanism with piezoelectric materials and that energy can be stored and used to supply power to other devices [3]. On the other hand, when an electrical field is applied a mechanical deformation is produced. Piezo-film has the capacity to create enough electrical energy that can be stored in a rechargeable battery for future use for a necessary situation where there is no power supply is present and need your mobile for just few minutes more [4]. Since, it is an energy revitalization device, it motivates walking and which makes it as an electrical health gadget which esteems physical fitness. There is one more characteristic that is integrated into this device, is a battery power bank which is used to store the extra charge that is produced when the phone is fully charged. After doing some research and consulting a medical staff, we have discovered that the pressure point of a foot is heel, mid foot, metatarsophalangeal joint, hallux, and the toes [5]. Consequently, piezoelectric crystals are placed into the maximum pressure points which are the sole and the heel of the shoes. In the brisk moment and vibrations on the crystals, the crystalized structured piezoelectric generates voltage pulses and minute quantities of current.
Keywords: Piezo Electric, Mobile charging, Piezo plate, Charging circuits, Electricity generation

ABSTRACT: This paper explores the integration of wireless power transfer (WPT) systems into electric vehicles (EVs) to address the growing demand for sustainable transportation solutions. Focusing on the application of a highly efficient half-bridge LLC resonant converter, the research aims to enhance the reliability and efficiency of EV charging systems through resonant operation. Employing MATLAB as the simulation platform, the investigation meticulously evaluates the performance of the WPT system under specified conditions, including a 230V AC input voltage and 1.1 kW output power. Critical parameters such as resonance frequency, output power, and power transfer efficiency are systematically examined to optimize the overall effectiveness and feasibility of the proposed WPT system within the context of electric vehicle applications. By addressing key technical aspects and utilizing advanced simulation tools, this research contributes to the ongoing discourse on sustainable transportation solutions. The findings presented herein offer valuable insights for the development of more efficient and reliable WPT systems in the pursuit of a greener and more sustainable future for electric vehicles.
Keywords: Wireless power transfer (WPT), Electric vehicles (EVs), Sustainable transportation, Half-bridge LLC resonant converter, MATLAB.

ABSTRACT: Global population growth intensifies pressure on food resources, emphasizing the need for sustainable food production. India, ranking third in global fish production, and being fourth-largest fish exporter, the country possesses over 10% of global fish diversity. In the fiscal year 2021-2022, 75% of India's fish production originated from inland fisheries, prompting the need for advancements in fish farming. Our project addresses this by establishing a miniature, automated fish farm to enhance safety, efficiency, and reliability, minimizing human intervention. Aquaculture, involving breeding and harvesting of aquatic organisms, leverages IoT, microprocessors, and automation. Using Arduino UNO and sensors, our project ensures optimal water quality by monitoring parameters like temperature, pH, Total Dissolved Solids (TDS), water color, and electrical conductivity. Automation includes an innovative feeder system with motors and servo facilities for a healthy feeding cycle, promoting fish growth without human intervention. Automatic pump systems, guided by real-time water turbidity and color detection, foster a conducive environment for aquatic life, saving time, money, and power. Emphasizing reliable automation, our project employs fully automated sensors, relays, motors, and a fish counting feature for real-time monitoring and control. This approach ensures precision, accessibility, and error-free management of the fish farm, contributing to wildlife conservation, especially for endangered species like Hilsa and Tuna which can later be extended for other wildlife creatures as well in their respective environment.
Keywords: global population growth, sustainable food production, automated fish farm, safety, efficiency, reliability, Arduino UNO, temperature, pH, TDS, water color, electrical conductivity, feeder system, turbidity and color detection, error-free, wildlife conservation