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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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Classifying Grape Leaf Disease using an Optimized YOLOv7 Deep Learning Model

Classifying Grape Leaf Disease using an Optimized YOLOv7 Dee...

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soft Computing for security Applications (ICsCsA), International Conference on

作者： John T Abraham Mohammad Manzoor Hussain R srividhya P Edith Linda s Karthik Nareshkumar R Department of Computer Science Bharata Mata College Kochi India Department of Computer Science and Engineering B V Raju Institute of Technology Medak Telangana India Department of Computer Science Dr G R Damodaran College of Science Coimbatore India Department of Computer Science Kristu Jayanti College Bengaluru India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The prevalence of grape leaf diseases has had a profound influence on the worldwide grape industry, resulting in considerable financial setbacks. For effective disease management, it is essential to classify the disease quickly and accurately. This research presents an enhanced YOLOv7 deep-learning model for accurately classifying grape leaf diseases. The dataset is obtained from Kaggle and consists of four categories: black rot, leaf blight, black measles, and healthy. The Adam optimizer is utilized to optimize hyperparameters in order to enhance the performance of the model. The refined YOLOv7 model achieved a remarkable accuracy of 99.24% on the test set, showcasing its potential for detecting grape leaf diseases at an early stage. The model in precision agriculture can aid in the management of grape production, categorization of leaf diseases, and monitoring of operations.

关键词： Deep learning Precision agriculture Accuracy Pipelines Production security Reliability Monitoring Optimization Diseases

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computer Aided Corn Leaf Disease Identification system 2

Computer Aided Corn Leaf Disease Identification System

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2nd International Conference on smart Electronics and Communication, ICOsEC 2021

作者： Noola, Daneshwari A Dayanand R, b. BLDEA's V P Dr P G Halakatti College of Engineering and Technology Information Science and Engineering Vijayapura India KSIT Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665433686

Automated corn leaf classification is important for the precise identification of corn leaf diseases and their type based on machine learning algorithms. Although, as mentioned above the manual detection of corn diseases is possible under a skilled botany exert. However, manual detection is extremely challenging and time-consuming process and always require a skilled pathologist to analyze plant behavior. As computer aided technologies and machine learning algorithms have widely evolved in recent time, especially, in the field of medical, defense, engineering, multimedia and agriculture. These techniques have the characteristics to process images and extract essential multimedia information from those images. Thus, segmentation and classification process are utilized to extract necessary information from the leaf images and to identify leaf disease and their type using that information. Further, utilization of machine learning techniques supports in detection of patterns for leaf images in computational way. several researchers have shown immense interest in identification of varied diseases in leaf images through machine learning and computer aided techniques. In this paper, a computer aided corn leaf disease identification system is proposed for accurate disease identification system. The proposed model is compared with the traditional methods and the results show that the proposed model accuracy is better than the traditional models. © 2021 IEEE.

关键词： Classification (of information)

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surveillance video-based Object Detection by Feature Extraction and Classification Using Deep Learning Architecture 11th

Surveillance Video-Based Object Detection by Feature Extract...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Akhmetshin, Elvir sultanova, sevara Anupama, C.s.s. Kumar, Kollati vijaya Lydia, E. Laxmi Department of Economics and Management Kazan Federal University Elabuga Institute of KFU Elabuga423604 Russia Department of Construction Urgench State University Urganch220100 Uzbekistan Department of Electronics and Instrumentation Engineering V.R. Siddhartha Engineering College Vijayawada520007 India Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam530049 India

ISBN: (纸本)9789819967056

As of late, deep learning has accomplished top exhibitions in object recognition undertakings. be that as it may, continuously, frameworks having memory or processing restrictions extremely wide and profound organizations with various boundaries comprise a significant impediment. Profound gaining-based object location arrangements arose out of PC vision has spellbound undivided focus as of late. This examination proposes novel method in observation video-based object location by highlight extraction with characterization utilizing profound learning. Here the info information has been gathered as observation video and handled for commotion expulsion, smoothening, standardization. Then, at that point, the handled video has been separated and ordered utilizing concealed convolution fluffy perception brain organizations. The exploratory examination has been completed as far as exactness, accuracy, review, F-1 score, RMsE, NsE. Proposed method attained accuracy of 91%, accuracy of 84%, review of 86%, F-1 score of 76%, RMsE of 61%, and NsE of 48%. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Object detection

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LsTM based Character Recognition for Text Extraction in Real-Time Natural scenes Images

LSTM Based Character Recognition for Text Extraction in Real...

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Data science and Network security (ICDsNs), International Conference on

作者： vishnu Kant soni Praveen Chouksey s. R. Tandan Amit Pimpalkar Rohit Miri Neetesh Kumar Nema Department of Computer Science and Engineering Dr. C. V. Raman University Bilaspur C.G. India Department of Computer Science Government R. V. R. S. Kanya Mahavidyalaya Kawardha C.G. India Department of Computer Science and Engineering (AIML) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering UTD CSVTU Bhilai C.G. India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Extracting text from complex real-world images poses a significant challenge in computer vision due to cluttered backgrounds, diverse fonts, and varying orientations. Traditional methods struggle with accuracy in such scenarios. This research introduces a novel text extraction model, Deep Convolutional Neural Networks (DCNNs), for precise character recognition. by combining Optical Character Recognition (OCR) with Long short-Term Memory (LsTM) networks, the model effectively handles sequential data and enhances text extraction performance. by integrating OCR with LsTM, the proposed approach aims to overcome the limitations of traditional methods and enhance text extraction performance in challenging scenarios. Evaluation of ICDAR datasets yielded a high average confidence score of 95.50%, highlighting the model's accuracy in challenging text extraction tasks. Real-time testing in bilaspur, India, demonstrated the model's robustness and adaptability, surpassing human annotations. These results highlight the potential of deep learning, particularly the OCR-LsTM fusion, for accurate text extraction in diverse natural scene images.

关键词： Adaptation models Accuracy Image recognition Text recognition Computational modeling Optical character recognition Real-time systems Character recognition Data mining Long short term memory

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Advancing Data science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jahir Pasha, M. Gaurav, Kumar bhanja, Amit Kumar shamout, Mohamed Dawood Mupparaju, Chinna babu Manohar bhanushali, Mahesh G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Lovely Professional University Phagwara India School of Management Sciences Department of Management Varanasi India College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates University of Central Missouri Masters in Computer Science warrensburg United States Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane India

ISBN: (纸本)9798350357769

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal Language Processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (Cv), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (Cv) is another region in which advances in AI-pushed information science are being v.s.ble. Cv generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. computerized facts analysis is one of the maximum powerful approaches. © 2023 IEEE.

关键词： Analyzing Records computerized Data Evaluation semantic Algorithms

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Online banking security: An Experimental study of Transaction Authorization Usability

Online Banking Security: An Experimental Study of Transactio...

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New Frontiers in Communication, Automation, Management and security (ICCAMs), International Conference on

作者： J siva Ram Prasad K. Parish venkata Kumar s srihari Reddy Pagadala Chaitanya E. s. R. Ravi Kumar G venkata Ramana Reddy Department of maths V. R. Siddhartha engineering college Vijayawada India Department of Computer Science and Engineering Brindavan Institute of Technology & Science Kurnool India Department of Computer Science and Engineering St.Martin’s Engineering College Dhulapally Secunderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The security measures used for online banking have undergone significant transformations throughout the comparatively short duration of its existence. The first implementations of authenticating and identity management systems were, and in some cases continue to be, susceptible to various assaults, including phishing. One of the current state-of-the-art options is using out-of-band channels for user re-authentication before each transaction. This article details the findings of a security analysis of such a system. According to the study results, it is secure against specific threats while being open to others. As attackers continue improving their methods, the remaining vulnerabilities will likely be taken advantage of in the future. Measures that might be taken toward against such assaults in the future are detailed.

关键词： Authorization Online banking Phishing Current measurement Hazards Fraud security

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Enhancing House Price Prediction Accuracy using Deep Learning and Hyperparameter Tuning with AEDA 7

Enhancing House Price Prediction Accuracy using Deep Learnin...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Priya, R.Padma Raghuveer, K. srinivas, K. basi Reddy, A. shriidhar, P.J. shirisha, N. Avinashilingam Institute For Home Science And Higher Education For Women School of Engineering Department of Electronics And Communication Engineering Tamilnadu Coimbatore India V.R.Siddhartha Engineering College Department of Business Management Andhra Pradesh Kanuru20007 India Secundrabad Telangana Dhulapally India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Cse Tamilnadu Salem India Mlr Institute of Technology Department of Cse Hyderabad Telangana Dundigal India

ISBN: (纸本)9798350371406

Traditionally, real estate agents and appraisers used their expertise and market knowledge to estimate house prices. However, due to the expansion of data sources and developments in data science, there was a growing interest in developing predictive models to automate and enhance the house price prediction process. This research introduces the Adaptive Exploratory Data Analysis (AEDA) tool, specifically the AEDA by frequency for categorical values, implemented in Python. The tool provides an API that maps usual AEDA tasks in statistical modeling for generating plots and statistics. It automatically generates insights and establishes standards and procedures. The frequency of users in various variables highlights the challenges in data processing pipelines for predicting house prices using Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also discusses the evaluation of the hyperparameter tuning model for house pricing in the subsequent chapter. Model building, in the context of ML, involves creating a model that can learn from data without explicit instructions. Regression analysis is utilized to observe and resolve prediction issues. The suggested house price prediction model combines a joint self-attention mechanism with a specific feature parameter representation. While buyers and sellers focus on external features like building area, location, and construction year, the proposed model emphasizes internal features. It emphasizes the importance of hyperparameter tuning in ML and DL algorithms to improve model accuracy by minimizing residual errors during validation. © 2023 IEEE.

关键词： Forecasting

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Modelling of an Intelligent voice system using MI Algorithm for E-business 5

Modelling of an Intelligent Voice System using MI Algorithm ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kiruthiga, v. Kondapalli, sunanda Gupta, varun Aarthy, R. sasidevi, J. sasthi Kumar, C. SRM Institute of Science and Technology Vadapalani College of Management TamilNadu Chennai India Jazan University Department of Computer Science Jazan Saudi Arabia Sant Baba Bhag Singh University Department of CSA Punjab Ludhiana India B.S.Abdur Rahman Crecent Institute of Science and Technology Department of Computer Science and Engineering TamilNadu Chennai India K. Ramakrishnan College of Engineering Samayapuram Department of Computer Science and Engineering TamilNadu Trichy India Karpagam Academy of Higher Education Department Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350398267

The next generation will require of such commerce site with the renaissance of conventional technology to more current technology and use of advanced technology, where a large portion of e-commerce are presently created on back of such revolution and inventive breakthroughs like robotization. Most of businesses or owner(s) of these sites are sending off more unique sites rather than conventional list and static sites, so machine learning, augmented with man-made brainpower, and deep learning, which are in demand and profoundly necessary, should be integrated into the present e-commerce sites to be best and profitable for the owners of these sites. The importance of artificial intelligence in various spheres of business and life has increased as a result of the accelerated technological advancement. The use of AI in voice recognition helps organisations and individuals understand how to provide stakeholders with better services and effectively completes the task. Therefore, this work focuses on utilising regression analysis to analyse the critical factors of employing AI in speech recognition for a successful multipurpose Machine Learning platform. © 2022 IEEE.

关键词： speech recognition

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sky sentinel: Pioneering Real-Time Facial Recognition in Intelligent Drones 4

Sky Sentinel: Pioneering Real-Time Facial Recognition in Int...

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4th International Conference on smart Electronics and Communication, ICOsEC 2023

作者： visalatchi, L. Anjelin, Praveena Eswaran, Malathi vijayalakshmi, K. sathish Kumar, L. Inamdar, Chinmay Dr.Umayal Ramanathan College for Women Department of It Tamil Nadu Karaikudi India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Cse India Kongu Engineering College Perundurai Tamil Nadu Erode India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai603203 India Bhopal University Scse VIT Madhya Pradesh India Vit Bhopal University Department of Computer Science and Engineering Madhya Pradesh India

ISBN: (纸本)9798350300888

The goal of this article is to provide a method that makes use of a face recognition-powered unmanned aerial vehicle (UAv). The goal of this approach is to make it easier for law enforcement to identify suspects, track down missing persons, verify identities, and carry out age-based monitoring. A facial recognition system is an electronic tool that uses AI to recognise people by their faces. Identity verification often makes use of facial recognition technologies. The facial features of a picture are analysed in this procedure. This research details the steps required to design, build, and test an unmanned aerial vehicle (UAv) integrated with a camera and face recognition software. The UAv is used as a remote control for a robot equipped with bluetooth technology. The suggested method proves it can adapt to different drone orientations. Drone camera angles in all 37 directions have an accuracy of 99.3 percent, proving this point. Recovery activities, rescue missions, occurrence inquiries, offender assessment, and crowd surveillance are just a few examples of how specialised software may aid law enforcement in their investigative work. © 2023 IEEE.

关键词： Drones

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