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IoT Based Poultry Farm Smart Management System

IoT Based Poultry Farm Smart Management System

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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Krishnamurthy, K.T. Managuli, Manjunath Malipatil, Sushma Bagyalakshmi, K. Salake, Sudha V. Kadalagi, Pankaja S. Patil, Sheetal B. Bms College of Engineering Vtu Dept. of Electronics & Instrumentation Belagavi India Kls Gogte Institute of Technology Vtu Dept. of Electronics & Communication Belagavi India Amc College of Engineering Vtu Dept. of Computer Science Belagavi India Sri Ranganathar Institute of Engineering & Technology Dept. of Electrical & Electronics Coimbatore India Kls Gogte Institute of Technology Vtu Dept. of Computer Science Belagavi India Sgb Institute of Technology Vtu Dept. of Computer Science Belagavi India

ISBN: (纸本)9798350359688

One of the greatest significant and fastest-growing financial sectors of India's agriculture industry at the moment is poultry. Chicken output has been steadily rising globally due to superior manufacturing processes and uniform farming management. Automation is essential in today's environment, and the Internet of Things (IoT) is a rapidly developing idea. Traditional systems are being transformed into automated systems. The project's main goal is to automate chicken farms by utilizing Internet of Things technology to carry out several management-related tasks. This study examines how a poultry farm's environmental factors, similar to humidity, temperature, and ammonia gas, are automatically observed and managed to promote chicken growth. The water position is also controlled and covered with the help of a detector module. All detector values are acquired by connecting all the detector modules to the microcontroller, and also, using the WIFI module, they will be uploaded to the webpage. Being in the care of the loud ranch can get the inner eco-friendly condition over a PC using the internet. This system will control temperature, moisture, ammonia gas and water situation with the help of a cooling addict, exhaust addict, ventilation window and DC motor without any moral interface. Grounded on the threshold values, it'll switch on the bias. therefore, this system design provides automated flesh, reduces the pool, and increases the product of healthy cravens. © 2024 IEEE.

关键词： DC motors

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Optimizing Mobility IoT Device Networks with Dynamic RIS in mMIMO-Cooperative NOMA 6G systems 1

Optimizing Mobility IoT Device Networks with Dynamic RIS in ...

引用

1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Hassan, Khalil Hassan, Mohamed Hamid, Khalid Hassan, Elmuntaser Mokhtar, Rania A. Saeed, Mamoon M. Dept. of Comm. Systems Uni. of Science & Technology Omdurman Sudan Dept. of Electrical Eng. Omdurman Islamic Uni. Omdurman Sudan Dept. of Computer Science Al-Neelain University Khartoum Sudan Department of Computer Engineering Taif University Taif Saudi Arabia Yemen

ISBN: (纸本)9798331533557

Advanced technologies like reconfigurable intelligent surfaces (RISs), massive multiple-input multiple-output (mMIMO), and non-orthogonal multiple access (NOMA) are crucial for the upcoming 6G communications landscape, particularly for IoT applications. This study uniquely explores the effects of RIS dynamics on the performance of mMIMO downlink (DL) cooperative NOMA systems in dense IoT environments. It introduces a novel methodology for modeling dynamic traffic distribution and integrating RIS, focusing on key performance metrics such as network capacity, spectral efficiency (SE), latency, and energy efficiency (EE). Various scenarios are evaluated, considering different distributions of IoT devices and signal-to-noise ratios (SNRs). Using simulation software, the research analyzes performance under unstable channel conditions with varying device distances, speeds, and power locations, employing 256-QAM modulation and successive interference cancellation (SIC). The results demonstrate that dynamic RIS significantly enhances capacity, spectrum efficiency, latency, and EE, paving the way for optimized IoT network architecture in future wireless systems. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization 8

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Papanashi, Suhas Chadaga, Manya Kshithi, R. Huddar, Santosh S. Sreelakshmi, K. Ramakanth Kumar, P. Rv College of Engineering Dept. of Electronics and Telecomm Bengaluru India Rv College of Engineering Dept. of Computer Science & Eng. Bengaluru India

ISBN: (纸本)9798331505462

Traffic congestion poses a significant challenge in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Bengaluru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selected junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively. © 2024 IEEE.

关键词： Investments

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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Enhancing Arecanut Farming Profits through Technological Advancements: A CNN-based Approach for Efficient Grading and Sorting 5

Enhancing Arecanut Farming Profits through Technological Adv...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Dhanush Ghate, D. Pallavi, K.N. Poojari, Asmita NMAM Institute of Technology Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350395235

Technological advancements can have powerful impact on the economic growth of agriculture in India. Areca nut farming is one such terrain of agriculture where investment in technology and automation can elevate the profit from farming by bringing down production cost. Areca nut is the major crop of Karnataka having roots in fifteen different districts. The grading and sorting of areca nuts is an important step in the production of areca nuts and is traditionally done by man power. People are trained to identify good and bad areca nuts, but the time taken for sorting areca nuts is more and it is not feasible for a large quantity of areca nuts. In order to minimize the time consumed, this method is introduced which classify the areca nuts into four different grades based on their quality. © 2024 IEEE.

关键词： Sorting

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-based Automatic Pan Evaporimeter Monitoring and Irrigation Control System for Crops 8

IoT-based Automatic Pan Evaporimeter Monitoring and Irrigati...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Kabir, Md Shahriar Niloy, Khalid Mahmud Hossen, Md Imon Afrose, Sumaiya Mofazzol, Md. Ismail Hossain American International University-Bangladesh Dept. of Electrical & Electronics Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350376425

This research study intends to automate the manual pan evaporimeter system and scheduling irrigation by considering soil water content and crop evapotranspiration. Automating the pan evaporimeter means performing tasks without human intervention, such as measuring the daily water evaporation amount, calculating and storing data, and refilling the pan with water. A waterproof ultrasonic sensor was used to measure the water evaporation amount. The ultrasonic sensor was installed on an innovatively designed structure facing the pan's water surface. The sensor measured the distance between the water surface and the sensor. The surface level decreased as the water evaporated over time, which caused an increment in the distance between the ultrasonic and water surfaces. By this, the sensor calculated the daily evaporation amount. The pan needed to be refilled when all water evaporated;a submersible pump was used to refill the pan automatically. A relay switch controlled the pump, and a microcontroller turned on the switch based on sensor value. The entire setup was IoT-enabled to monitor and control the system remotely on a mobile device. The entire system was backed up by solar energy to run on clean energy. A solenoid valve was used to control the flow of irrigation water. Pan evaporation estimated the amount of water that could be lost owing to evaporation and transpiration. Soil moisture sensors provided a direct measurement of the amount of water present in the soil. The two combined generated a dynamic and responsive irrigation plan that maximized water use and promoted the healthy development of crops. That approach was used. The ultrasonic sensor accurately measured water evaporation, but the installation process was complex. So, a need arises to develop a simple system or scale-type sensors to overcome the complexity. © 2024 IEEE.

关键词： Solenoid valves

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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SPEI Based Drought Prediction and Validation Using Machine Learning Algorithm in Bangladesh from 1981 to 2017

SPEI Based Drought Prediction and Validation Using Machine L...

引用

2024 IEEE International Conference on Signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Mashuq, Md Farhan Akther, Juthy Das, Krishna Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (纸本)9798331510213

A natural event known as a drought is defined by a long period of insufficient rainfall and limited access to water supplies, which means there isn't enough water for many uses, including agriculture, industry and domestic consumption. The Standardized Precipitation Evapotranspiration Index (SPEI) is a frequently utilized indicator in drought monitoring. In this work, from 1981 to 2017, temporal variation at Ishwardi upazila was used to evaluate and analyze the SPEI's performance at 1, 3, 6 and 12-month timeframes. An analysis is carried out to see how well machine learning approach such as Support Vector Machine (SVM), Random-Forest (RF), Decision-Tree (DT), Extreme Gradient Boosting (XGB) and K-Nearest Neighbors (KNN) work in forecasting drought conditions. The prediction outcomes of all five techniques were assessed using four performance measures: the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE). The proposed methods provided good results and can serve as a quick decision-making tool for water management. © 2024 IEEE.

关键词： Support vector machines

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