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Applying Deep Learning and Transfer Learning Techniques for Improving Animal Intrusion Detection in Agriculture Farms

Applying Deep Learning and Transfer Learning Techniques for ...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Saraswathi, R. Shobarani, G. Subramani, A. Tamilarasan, D. Sreenivasa Institute of Technology and Management Studies Department of Master of Computer Applications Andhra Pradesh Chittoor India Islamiah Women's Arts and Science College Department of Artificial Intelligence Thiruppatur Tamil Nadu Vaniyambadi India M.V.Muthiah Government Arts College for Women Department of Computer Science Tamilnadu Dindigul India Sri Venkateswara College of Engineering and Technology Department of Master of Computer Applications Andhra Pradesh Chittoor India

ISBN: (纸本)9798350348910

Due to the nature of farmlands or provincial landscapes, monitoring is required to keep unauthorized persons out of the region and to safeguard the district from wildlife. Various techniques point straight towards perception, which is primarily for human gatecrashers, but we sometimes forget that the primary opponents of such farmers are the animals who trash the crops. Crop damage caused by monster attacks is one of the most serious dangers to collect yield. Crop striking is becoming one of the most distant human-natural life conflicts as created land expands into formerly untamed life domain. The principle is compelling and forceful checking of the wild creatures' squarely at home. This research paper is focused to develop computational techniques for identifying the animals that swoop into the agricultural area. Because there are so many various kinds of organisms, genuinely recognizing them may be a difficult task. In this paper, we have used a camera to capture routine images of the surrounding area and to observe the entire homestead at regular intervals. With the aid of further developed learning models, we can distinguish between different stages of a creature's development and play appropriate noises to frighten them off. The various convolutional brain network libraries and ideas that went into creating the model are acknowledged in this study. We have worked on the path of creature interruption discovery in agribusiness areas using deep learning and move learning techniques. © 2023 IEEE.

关键词： Intrusion detection

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Vision-Based Locomotion Control for Humanoid Robots: A Study on Vision-Guided Walking Strategies

Vision-Based Locomotion Control for Humanoid Robots: A Study...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Pandey, Saroj Kumar Kumar, Lalan Kumar, Gaurav Kumar, Ankit Singh, Kamred Udham Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Recently, there has been a lot of research on using vision to direct humanoid robots as they walk. Focusing on the major methodologies, difficulties, and prospects, this review article presents an overview of the current state of study in the vision-guided walking of humanoid robots. In the first part of the study, we introduce vision-guided walking and go through the several methods that have been developed to execute it, such as visual serving, visual odometer, and visual simultaneous localization and mapping. The difficulties of vision-guided walking are then discussed;these include, but are not limited to, the necessity for accurate and strong computer vision algorithms, the robot's need to have a solid awareness of its own body and how it moves, and the need to maintain balance while walking. This paper provides a detailed review of the research on humanoid robots that use vision to direct their gait. Deep learning for vision-guided walking, tactile and proprioceptive sensing combined with vision, and reinforcement learning for adaptive walking are only a few of the issues covered in this overview. Future directions in vision-guided walking research are discussed in this paper. These include the development of more advanced and reliable computer vision algorithms, the incorporation of other modalities like tactile and auditory sensing, and the investigation of new applications for vision-guided walking in fields like healthcare and rehabilitation. © 2023 IEEE.

关键词： Anthropomorphic robots

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Virtual Mouse Using Face Gesture and NLP 2

Virtual Mouse Using Face Gesture and NLP

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

作者： Zad, Ajay Jagannatha, S. Kumar, Manish Ajitha, S. Prakash, B.R. M S Ramaiah Institute of Technology Department of Masters of Computer Applications Bangalore India East Point College of Engineering Department of Computer Applications Bangalore India M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

The mouse plays a very important role in interacting with laptop's and computer system, to perform necessary operations. In this paper, we are proposing a method to carry out mouse operations on the computer system without a physical mouse by utilizing voice commands and face-motion gestures. The system employs an internal webcam to detect facial gestures and an internal microphone to receive voice commands from the user. By employing a virtual mouse based on face gestures and natural language processing (NLP), we can accomplish all the fundamental functionalities of a mouse by issuing voice commands to the computer system. The system accepts these commands and executes corresponding mouse events such as "click,""right-click,""double-click,""scroll-up,""scroll-down,""minimize,"and "close."The mobility of the mouse cursor on the monitor is achieved through face motion gestures, enabling the cursor to move up, down, left, and right, respectively. © 2023 IEEE.

关键词： Natural language processing systems

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Durability Prediction Model of Reflector Material Against Solar Energy in Corrosive Environment

Durability Prediction Model of Reflector Material Against So...

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2022 International Conference on science and technology, ICOSTECH 2022

作者： Manurian, Wahyu Padeli Supriati, Ruli Cahyadi, Dede Cesna, Galih Putra University of Raharja Master of Informatics Management Tangerang Indonesia University of Raharja Master of Computer Science Tangerang Indonesia University of Raharja Master of Technology Information Tangerang Indonesia University of Raharja Department of Informatics Engineering Tangerang Indonesia

ISBN: (纸本)9781665479851

Concentrated solar energy technology plays an essential role in today's energy turnarounds. Face our society. The energy generated by this technology is highly dependent on the optical behavior of the reflector material of the solar cell. Corrosion of solar reflectors can be a problem in areas with high corrosion levels Severe environments, such as excessive corrosion of solar mirrors can have a devastating impact on the feasibility of centralized solar thermal systems. This study focuses on modeling the shelf life of four different things. Sunlight reflective material exposed outdoors by accelerated deterioration test. For this purpose, ten suitable locations to concentrate solar thermal applications were classified according to their corrosive aggression. Commercially available free leed and low-cost reflector samples were exposed everywhere to determine the effect of corrosion on durability. Corrosion defects on the solar reflector during outdoor exposure Reproduced correctly in the CASS test. A new life prediction model has been developed for all solar reflectors Depending on the corrosive aggression of the place. Number and thickness of paint layers used in Solar mirrors are recognized as essential parameters for improving energy production. Identified CSP system in a corrosive environment. Expected to reduce capital investment in purchasing solar mirrors For places with low corrosiveness. © 2022 IEEE.

关键词： Durability

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Leveraging Machine Learning for Next-Generation Cyber Security

Leveraging Machine Learning for Next-Generation Cyber Securi...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Haripriya, V. Sharma, Vishal Dari, Sukhvinder Singh Nivedha, C.S. Tiwaskar, Shweta A. Singh, Akhilendra Pratap Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350329773

This paper outlines how machine learning can be used to enhance subsequent-generation cyber safety. We move over how the system gaining knowledge of events may be used to locate unusual behavior or ability threats faster than conventional techniques. We further discuss how the gadget mastering era can research from beyond located styles to better guard towards future security threats. Furthermore, we discuss how device getting to know can be applied to optimize cyber safety approaches with a purpose to make sure the best possible protection for systems. Finally, we talk some ability challenges that industry players should do not forget whilst leveraging gadget gaining knowledge of for subsequentera cyber protection. © 2023 IEEE.

关键词： Cybersecurity

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An Intriguing and Predictive Machine Learning Method for the Detection and Identification of Diabetes 12th

An Intriguing and Predictive Machine Learning Method for the...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Srinivasula Reddy, A. Raja, Mandapati Tejaswini, V. Ramesh Kumar, R. Sri Vidya, G. Ragini, K. Department of Electrical and Electronics Engineering CMR Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering CMR Engineering College Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Master of Business Administration CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

According to WHO estimates from 2016, diabetes has been named one of the chronic illnesses with the greatest rate of growth and is the seventh largest cause of mortality. For a clinically significant outcome, early identification of diabetes is always preferred due to the relatively long asymptomatic phase. Diabetes is a leading cause of stroke, heart attacks, kidney failure, blindness, and amputation of lower limbs. If it is discovered in its early stages, they could be prevented. The digital age has made an enormous quantity of clinical data available, which has allowed deep learning algorithms to provide accurate medical diagnosis and prognosis. To train the machine to predict it, we employ the diabetes dataset. The dataset was studied using the random forest, deep neural network, and logistic regression algorithms, together with a DNN that contained embedded data for the categorical features. With a F1 rating of 1.0 on test data, the DNN with insertions correctly predicts the majority of test set examples, or almost 100% of the cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks 6

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Venkateswaran, N. Ramprabu, J. Marimuthu, Ramaprabha Ramana, Nagella Venkata Kotha, Mahesh Mallala, Balasubbareddy Department of Master of Business Administration Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Management Studies Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle517325 India CMR Technical Campus Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331522667

With the improvement in EV’s popularity, controlling the load demand of EVCS’s has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management. © 2025 IEEE.

关键词： Charging stations

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Proposing Machine Learning as the technology to Deal with the Liver Disease

Proposing Machine Learning as the technology to Deal with th...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Yadav, Nageshwar Dev Singh, Vaibhav Kant Research & Development Product Specialist at Informatica Master Data Management Cloud Product Karnataka Bengaluru India A Central University School of Studies of Engineering and Technology Guru Ghasidas Vishwavidyalaya Department of Computer Science & Engineering Chhattisgarh Bilaspur India

ISBN: (纸本)9781665492942

In this work the Authors majorly focuses on the Disease that is Liver Disease and tried to work out a Strategy that could be handy to Deal with the Problem under Question. The implementation of the Work is solely in Python. The Major Python Libraries are used to come up with a Solution. There are various ML approaches Available to Handle the Problem. In our Implementation we made a Utilization of the Data-Set received from Kaggle Repository. The Implementation makes usage of the technology that is Support Vector Machine. We in our work made utilization of the Four Kernels Available for Implementation in Python Programming. In the Implementation Section we will observe the Classification Report Generated in the Coding done by the Authors. SVM is a very nice approach to take care of the Medical Issues. © 2023 IEEE.

关键词： Python

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