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IoT based Animal Trespass Identification and Prevention System for Smart Agriculture 7

IoT based Animal Trespass Identification and Prevention Syst...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Usharani, Shola Gayathri Rajarajeswari, S. Kishore, D.S. Depuru, Sivakumar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept of CSSE Tirupati India

ISBN: (纸本)9798350397253

Unlike the developed countries, there are several problems faced by the Indian farmers. One of the major problems dealt by the Indian farmers is the trespassing of animals into their agricultural fields. Animal Trespassing is a common phenomenon especially in farmlands near forest and hilly areas. There are a lot of cases where animals trespassed the farmlands and had destroyed the crops, causing great losses and distress to farmers. The most affected where poor farmers, who depend completely on farming for their income. This being a threat to many farmers, considering the loss that they would incur. Many of the farmers are dependent on government's compensation, where getting compensation from government is a highly tiring and tedious process, and also in most of the cases the compensation paid by the government would be much lesser when compared to the losses suffered by the farmers. So, in order to prevent such animal trespasses, farmers use few traditional methods like bursting crackers, building trenches, building electrical fences, etc., where the efficiency is low and also the animals are harmed. So, there is a need for an innovative animal trespass prevention system that is efficient and also not harmful for the animals. With the rapid growth and penetration of technology, it is easy and cost efficient to use the existing modern techniques in technology to deal with animal trespass into the farmland. Considering the growth of IoT technology and its compatibility with agricultural use cases makes it one of the best options to build agricultural tools and devices. The Proposed work is based on IoT Technology, which is a cost-effective Arduino based IoT prototype that would enable the end user (The Farmer) to protect and monitor his/her cropland remotely using various sensors and IoT hardware modules to deal with the problems of animal trespassing into the fields, with better efficiency, without harming the animals and hence prevent any major losses. It is a co

关键词： Animals

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Benchmarking Manufacturing Execution System (MES) Deployed Across Automotive Manufacturing Industries Towards IOT 4.0

Benchmarking Manufacturing Execution System (MES) Deployed A...

引用

2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Chellaboina, Srinivas Arvind, A.R. Geetha, Angelina Hindustan Institute of Technology and Science Dept of Computer Science and Engineering Chennai India Ashok Leyland Chennai India

ISBN: (纸本)9781665425773

MES (manufacturing execution system) is the state-of-the-art vertically integrated business system used in manufacturing industries for managing operations, materials, inventory, resource, maintenance, tracking, and traceability of the product under manufacture. High volume manufacturing uses multiple complex modules to handle the operations. This paper focuses on the study of MES systems deployed across manufacturing industries towards IOT 4.0, citing industry challenges, manufacturing strategies, technology fit, and derived benefits. © 2022 IEEE.

关键词： Internet of things

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Mental Health Tracker Using Machine Learning Approaches 4

Mental Health Tracker Using Machine Learning Approaches

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Thotad, Puneeth N. Kallur, Shanta Mundaragi, Laxmi Kadam, Swati H. Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user's mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively. © 2023 IEEE.

关键词： Decision Tree Digital Mental Health K-Nearest Neighbor Logistic Regression Machine Learning Random Forests

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

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12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and depth images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Integrated IoT Framework with Classification Approach for Medical Data Analysis 9

A Novel Integrated IoT Framework with Classification Approac...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Bikku, Thulasi Sree, K. P. N. V. Satya Jarugula, Jyothi Sunkara, Mounika Vignan's Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India Usha Rama College of Engineering and Technology Dept. of Computer Science and Engineering Vijayawada India Usha Rama College of Engineering and Technology Department of Cse Vijayawada India

ISBN: (纸本)9789380544441

In the healthcare systems, usage of advanced integrated technologies like Internet of things (IoT) and Machine learning (ML) techniques were limited. Different amalgams of IoT devices and ML mechanisms are available for medical sector but are limited to certain domains only. These models either provide patients current state data or specific domain analyzing and surveillance pre/post treatment data like heart or brain functions with corresponding medical aid. Also data available is used as clinical study for medical professionals and for better understandings of patients about their state. Specific domain gadgets' like wrist bands or smart bands uses some sensors about vital, temperature and pulse etc., checkups are available but they were not meant for diagnosing or for treatments. In this paper, we proposed an integrated model to use IoT and ML algorithms for a healthcare system. Tracking of patients' status can be done using some sensors such as lightweight, portable, and low-powered sensor nodes. These Sensors sense the patient's status and send the parametric data to the central controller, to take actions during the critical condition of the patients. The data sent to the controller always provided in secure and encryption form. At the same time, patient data is sent to doctors, so that they can provide the instructions to the caretakers of the patients with quick and proper solutions in real-time. For disease prediction, our model uses supervised machine learning algorithms, In order to get the efficient feature set and improve the better accuracy, and pre-processing techniques to eliminate features that are irrelevant, missing values and outliers from biomedical data which aids in better disease prediction. To further strengthen the proposed integrated model design is compared with various traditional classification algorithms to specify its improved accuracy and computational time for accurate prediction of the patient's disease and acts as a decision suppo

关键词： Internet of things

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AI-Driven Dynamic Network Slicing for Resource Optimization in 5G Networks: Implementation and Performance Evaluation

AI-Driven Dynamic Network Slicing for Resource Optimization ...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Ayushi Jain Prateek Jain Aditya Verma Lisa Gopal Preeti Chaudhary Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Scholar Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

We introduce in this paper an AI-based dynamic network slicing model for resource allocation targeting 5G networks. It does this by using machine learning algorithms that help the model learn and adapt to live traffic conditions so as to intelligently manage different service types, including eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communication) and mMTC (massive Machine Type Communication). This leads to substantial improvements in three of the four core performance metrics, latency, throughput and energy efficiency (at a low sacrifice of when it comes to QoS adherence). Their work also demonstrates that AI technologies hold the promise for revolutionary network management, offering a scalable solution that can address ever-increasing requirements of next-generation telecommunications, by surpassing the capabilities of well-established static slicing techniques.

关键词： Adaptation models Network slicing Quality of service Ultra reliable low latency communication Dynamic scheduling Throughput Telecommunications Resource management Artificial intelligence Optimization

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Cybersecurity: Threats, Requirements, Deployments, Techniques, System Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

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Smart Devices (ICSD), International Conference on

作者： Ajay Yadav Vijay Karnatak Amit Kumar Mishra Shalini Verma Pallaw Singh Aswal Neha Tripathi University Institute of Computing Chandigarh University Mohali Punjab India Dept. of Computer Science & Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Doon Group Of Institution Rishikesh India Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users’ data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport Systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided.

关键词： Authorization Smart cities Systems architecture Cyberspace Loss measurement Fraud computer security Protection Smart devices Business

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Credit Card Fraud Detection: Data Balancing Techniques and Model Interpretability

Credit Card Fraud Detection: Data Balancing Techniques and M...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanveer Kaiser Avishek Chowdhury Umme Humyra Nabila Pantha Singha Mohammad Hasan M. A. Rasel Dept. of Computer Science & Engineering Premier University Chattogram Bangladesh Dept. of Computer Science & Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Dept. of Artificial Intelligence Universiti Malaya Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Credit card fraud detection is a crucial task in fi-nancial institutions to safeguard against fraudulent transactions. This study investigates the performance of Random Forest and Gradient Boosting models on a Nigerian credit card fraud detection dataset. Key features were selected using Chi-Square and Analysis of Variance (ANOVA) tests, and the models were evaluated across four data balancing techniques: no balancing (imbalanced), under-sampling, over-sampling, and hybrid sampling. The primary focus was on recall, as misclassifying fraudulent transactions can have significant financial implications. Gradient Boosting consistently outperformed Random Forest, achieving higher Recall, F1-Score, and AUC-ROC values across all experiments, particularly with over and hybrid sampling. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied to the Gradient Boosting model. SHAP analysis revealed that the amount of the transaction and average income expenditure were the most influential features in predicting fraud. Local and global explanations highlighted how specific feature values impacted the model’s decisions, while dependency plots uncovered interaction effects between key features. These findings contribute to the advancement of fraud detection methodologies, offering insights into effective strategies for mitigating financial risks associated with fraudulent transactions.

关键词： Analytical models Focusing Credit cards Boosting Prediction algorithms Data models Fraud Security Random forests Analysis of variance

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Machine Learning Classifiers and Hyperparameter Tuning to Enhance Prediction of ASD: An Automated Django Application

Machine Learning Classifiers and Hyperparameter Tuning to En...

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Communication & Computing (WCONF), World Conference on

作者： Masruk Habib Puvanenthira Rajah Ravikumar R N Sivakumar Nadarajan Santushti Santosh Betgeri Mahi Itagi Dept. of Computer Engineering Marwadi University Rajkot India Dept. of Computer Science and Engineering Alliance University Bangalore India Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India Dept. of Electronics and Computer Engineering Padre Conceicao College of Engineering Verna Goa

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Autism Spectrum Disorder (ASD) impacts social interaction, communication, and behavior as a result of variations in brain development of children. Typically, symptoms manifest around the age of 2 and encompass a decrease in eye contact during early infancy, a lack of response to one's name, indifference towards caregivers, a decline in developmental progress, loss of language abilities, and distinct behavioral patterns. The objective of this study is to assess the predictive accuracy of different machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Extra Trees (ET), by utilizing the UCI Repository dataset. We prioritize the target variable “Class/ASD” along with twenty other attributes, such as age, gender, and AQ-10 (Child version) outcomes. Prior to hyperparameter tuning, the SVM classifier achieved the highest accuracy of 0.949. The LR and ET classifiers both achieved perfect precision and recall scores, resulting in an accuracy of 0.991. After fine-tuning, the KNN, NB, SVM, LR, ET, and DT classifiers demonstrated noteworthy enhancements, with SVM, LR, ET, and DT achieved 100% classification metrics. This demonstrates the significant influence of hyperparameter tuning on improving the performance of the model. Finally, using Django Framework a web application is developed which allows the user to enter input data, DT model will process and predict the disorder.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Machine learning Medical services Nearest neighbor methods Predictive models Tuning Monitoring

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