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Intelligent systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： Gopika N Rani Divya Darshini R Monirhithikka S P Samsitha Banu S Saravu Miththira v C Subashini B Department of CSE PSG College of Technology Coimbatore India

Unlike diseases of the human body, plant diseases don't camouflage themselves within the body of the crop. The leaves reflect the infection with a change in color, shape, texture or a combination of the three. Hence, predictive modelling of the leaf images dataset using machine learning can help detect the disease. Mostly the image processing algorithms is used for predicting the diseases. The Random forest algorithm is used for color segmentation and Convolutional neural networks mainly focuses on disease classification. vision based plant infection showed efficient results and promising performance. The features are extracted such that they are feasible for a severity analysis. The percentage of severity is extracted from the percentage which is 94.6% of the classification accuracy after which suitable remedial actions are suggested. This offers a comprehensive strategy for addressing the threat of crop diseases that lower agricultural output and waste. (Choice of crop depends on accessibility of dataset).

关键词： Plant diseases Machine learning algorithms image color analysis Shape Crops Feature extraction Prediction algorithms

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Automated Traffic Sign Recognition via CNN Deep Learning

Automated Traffic Sign Recognition via CNN Deep Learning

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： K.v. Narasimha Reddy Yenuganti Narendra Medam Adi Nagamanendra Reddy Avula Ramu Dodda venkata Reddy Sireesha Moturi Dept of CSE Narasaraopeta Engineering College Narasaraopeta India Dept of CSE Narasaropeta Engineering College Narasaraopeta India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The rapid development of the road traffic systems is a very important component of the nation’s infrastructure, and that is reflected in growing importance of the traffic safety. Traffic Sign Recognition (TSR) is an important field of research because traffic offenses, particularly the disregard for traffic signs, are a major contributor to accidents. This paper gives a comprehensive review of the latest developments in the area of traffic sign detection and recognition techniques with attention to the applications of CNNs. This paper discusses the need for traffic signs to be detected under complex conditions and proposes an architecture based on modified CNN that helps reduce the processing time and increase accuracy. Improving recognition accuracy in realistic environments is the motivation behind the CNN model, which has been architected for real-time training as well as target identification. Experiments suggest that this solution outperforms present intelligent driving systems and the state-of-the-art performance achieved by the image processing algorithms currently in use and traffic sign datasets.

关键词： Training Technological innovation Accuracy Target recognition Road safety Real-time systems Road traffic Safety Convolutional neural networks vehicles

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran v Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and Uv exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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Secure vision: Enhancing Data Security in Wireless Sensor Networks through image processing

Secure Vision: Enhancing Data Security in Wireless Sensor Ne...

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

作者： B. venkata Swamy S. Gopi Kattupalli Sudhakar Pechetti Girish M. Janaki Rani Shrikant Upadhyay Department of Chemistry B V Raju Institute of Technology Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of AI & DS Lakireddy Bali Reddy College of Engineering Mylavaram Bonam Venkata Chalamayya Institute of Technology and Science Amalapuram Batlapalem Department of ECE Dr. MGR Educational and Research Institute Chennai Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, base station as well as the control center all of them played a vital role in functioning and managing of data. This network security was provided with a layered security approach like the physical, data link, network, transport, and application layers. Data security and integrity are secured through advanced image processing algorithms, and reliable communication protocol. Regular maintenance, constant monitoring and hardening solutions through resilience and fault tolerance mechanisms added a layer of trust in securing this WSN. A comparative analysis was made using different algorithms, such as Intrusion Detection systems (IDS) and detection accuracy is 98% and proposed model which has shown a better result in the energy consumption, data transmission latency, encryption time, detection precision as well as efficiency to insure an original data integrity.

关键词： Wireless communication Wireless sensor networks Protocols image processing Data security Scalability Network security Maintenance Monitoring Resilience

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Computer vision-Based Hand Recognition and Gesture Control for Dino Games

Computer Vision-Based Hand Recognition and Gesture Control f...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Balumuri Dinesh C v Naveeth Reddy N. Senthamilarasi Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hand Recognition and Gesture Control For Dino Game Using Computer vision to control the popular Chrome Dino game using hand recognition and gesture control through computer vision techniques. The system leverages real-time image processing and machine learning algorithms to detect and interpret hand gestures, allowing for an intuitive and interactive gaming experience. The proposed method utilizes a webcam to capture live video feed, from which hand landmarks are extracted using a pre-trained neural network model. various hand gestures, such as swipe and hold, are then mapped to corresponding in-game actions such as jumping and ducking. This gesture-based control mechanism not only enhances user engagement but also demonstrates the potential of computer vision in creating touchless interfaces for gaming applications.

关键词： Hands Computer vision Machine learning algorithms Sensitivity Webcams Scalability Lighting Games Streaming media Real-time systems

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Intelligence systems for Quality Assessment of Threaded Surfaces and Flaw Monitoring Based on Digital Light Field Recording

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RUSSIAN JOURNAL OF NONDESTRUCTIvE TESTING 2020年第11期56卷 915-926页

作者： Yu. Ol't Maksarov, v. v. Makhov, v. E. Estonian Univ Life Sci EE-51014 Tartu Estonia St Petersburg Min Univ St Petersburg 199106 Russia AF Mozhayskys Mil Space Acad St Petersburg 197198 Russia

The topical issues of constructing an intelligence system for assessing the quality of processing threaded surfaces and testing for flaws when manufacturing critical products based on light field (LF) recording have been considered. algorithms and methods have been developed for monitoring the main geometric parameters of the shape and of the surface defects in images obtained from a light field recorder by digitizing the images and morphologically analyzing image layers. An implementation is proposed for the developed algorithms on the platform of the National Instruments software using intelligence algorithms based on the principle of programmable logic. It has been shown that the accuracy of the results of determining the geometric characteristics with the proposed solutions allows one to diagnose technological equipment in the process of material machining.

关键词： intelligence systems quality of processing threaded surfaces inspection of thread defects light field recorder image contrast digitization morphological analysis of images shavings threading tool

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CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation systems 15

CLEAR: An Efficient Traffic Sign Recognition Technique for C...

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IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Rai, Meghna Khosla, Bhawna Dhawan, Yash Kharotia, Himanshi Kumar, Neeraj Bandi, Ajay Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Northwest Missouri State Univ Maryville MO USA

ISBN: (纸本)9781665460873

In this modern era, the popularity of autonomous systems has increased manifold because they are replacing people in various jobs and are expected to become the backbone of modern society. Specifically, self-driving vehicles and robots are gaining popularity in a wide range of applications. However, traffic sign detection, a critical component of intelligent transportation systems, remains quite challenging since it needs to be done quickly, with high precision, and with high dependability. A fast, real-time, robust automatic traffic sign detection and recognition can support and relieve the driver and significantly increase driving safety and comfort. Out of many algorithms and frameworks available for traffic sign identification, i.e., object detection, one of the most popular ones is You Only Look Once (YOLO) since it provides accurate results with minimal background errors in most real-time processing tasks and has excellent learning capabilities. Motivated by this, this research article provides a novel framework, CLEAR, for traffic sign identification in adverse climate conditions using YOLOv5. We compare different models' speed, accuracy, and other metrics on a Traffic Sign Dataset obtained from the Open image Dataset v6. Experimental results demonstrated that the proposed CLEAR model achieved the best performance with a Mean Average Precision of 0.73392 and Recall of 0.74194 compared to the existing schemes.

关键词： YOLO Traffic Signs Autonomous vehicles Object Detection image Classification Computer vision Deep Learning Real-Time Detection

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Spatial Attention Mechanism Integrated Ensemble Machine Learning for Real Time Prediction and Classification in Precision Agriculture

Spatial Attention Mechanism Integrated Ensemble Machine Lear...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： Myasar Mundher Adnan Bura vijay Kumar Madhura G K G. Mohammed Abhijeet Das Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Ahl Al Bayt University Karbala Iraq Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Generating spatial and temporal features for real time prediction and classification in precision agriculture is challenging due to inaccurate estimation of features from the remote sensing images. Using Geographical Information System (GIS) and systematic data regarding spatial and temporal data for monitoring and management of agricultural activities. The proposed research integrated GIS and used satellite image processing with Ensemble Machine Learning (EML) algorithms. Initially, the remote sensing data is pre-processed with normalization technique to remove noise. The pre-processed data is given as input to the Improved Speeded Up Robust Features (ISURF) for fast computation and identification of relevant features. The EML algorithms such as Random Forest (RF), Support vector Machine (SvM), and Artificial Neural Network (ANN) are used as a prediction and classification models considering the ISURF features as input. Additionally, the Spatial Attention Mechanism (SAM) is integrated with the EML algorithm to particularly focus on the spatial features of the remote sensing data. The experimental results showed that the proposed SAM-EML achieved precise prediction and classification with 98.30% accuracy, and 97.19% precision when compared to the existing methods Random Forest Regression (RFR) and RF-SvM.

关键词： Support vector machines Machine learning algorithms Attention mechanisms Computational modeling Artificial neural networks Prediction algorithms Classification algorithms Satellite images Remote sensing Geographic information systems

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Efficient Diagnosis of Intracranial Tumor on MRI Scans

Efficient Diagnosis of Intracranial Tumor on MRI Scans

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International Conference on Communications and Signal processing

作者： R.S. Amshavalli P RamKumar M Reshath B. Gracelin Sheena v. Nithish Kumar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The accurate and timely diagnosis of intracranial tumors is crucial for effective treatment and management. In recent years, magnetic resonance imaging (MRI) has emerged as a valuable tool for detecting and diagnosing intracranial tumors. The objective of this paper is to review the current state of research on the diagnosis of intracranial tumors using MRI scans and to provide an overview of the various techniques and methodologies employed. MRI has proven to be highly sensitive and specific in the detection of intracranial tumors. It provides detailed images of the brain, allowing for the identification of tumor location, size, and characteristics. various MRI sequences, such as T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced imaging, can be utilized to enhance the visualization and detection of tumor tissues. Moreover, advanced MRI techniques, including magnetic resonance spectroscopy (MRS) and functional MRI (fMRI), have shown promise in evaluating tumor metabolism and functional status. Several studies have focused on developing computer-aided diagnosis (CAD) systems to assist radiologists in interpreting MRI scans and improving diagnostic accuracy. These CAD systems utilize machine learning algorithms and image processing techniques to analyze MRI data and help distinguish between normal brain tissue and tumor abnormalities. Furthermore, the integration of MRI with other imaging modalities, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), has been explored to enhance tumor detection and characterization. In conclusion, MRI plays a crucial role in the diagnosis and detection of intracranial tumors. The development and utilization of advanced imaging techniques, as well as the integration of MRI with other modalities, hold great promise in improving early detection, accurate diagnosis, and effective treatment planning for patients with intracranial tumors.

关键词： Machine learning algorithms Systematics Magnetic resonance imaging Signal processing algorithms Medical services Planning Reliability

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Assessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia grading 35

Assessment of the association of deep features with a polyno...

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35th SIBGRAPI Conference on Graphics, Patterns and images (SIBGRAPI)

作者： Silva, Adriano B. de Oliveira, Cleber I. Pereira, Danilo C. Tosta, Thaina A. A. Martins, Alessandro S. Loyola, Adriano M. Cardoso, Sergio v. de Faria, Paulo R. Neves, Leandro A. do Nascimento, Marcelo Z. Fed Univ Uberlandia UFU Fac Comp Sci FACOM Uberlandia MG Brazil Sao Paulo State Univ UNESP Dept Comp Sci & Stat DCCE Sao Paulo Brazil Fed Univ Sao Paulo UNIFESP Sci & Technol Inst Sao Paulo Brazil Fed Inst Triangulo Mineiro IFTM Uberaba MG Brazil Fed Univ Uberlandia UFU Sch Dent Area Oral Pathol Uberlandia MG Brazil Fed Univ Uberlandia UFU Inst Biomed Sci Dept Histol & Morphol Uberlandia MG Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Oral epithelial dysplasia is a potentially malignant lesion that presents challenges for diagnosis. The use of digital systems in histological analysis can aid specialists to obtain data that allows a robust and fast grading process, but there are few methods in the literature proposing a grading system for this lesion. This study presents a method for oral epithelial dysplasia grading in histopathological images combining deep features and a polynomial classifier. The ResNet50 and AlexNet models were trained with the images and information was extracted from the convolutional layers, exploring convolutional neural networks via transfer learning. Then, the ReliefF algorithm was used to rank and select the most relevant features, which were given as an input to the polynomial classifier. The methodology was employed in a dataset with 296 regions of mice tongue images. The results were compared with the gold standard and other algorithms present in the literature. The classification stage presented AUC values ranging from 0.9663 to 0.9800. When compared to other algorithms present in the literature, our method provided relevant results regarding accuracy and AUC values. The proposed approach presented relevant results and can be used as a tool to aid pathologists in grading oral dysplastic lesions.

关键词： Tongue Histopathology Transfer learning Feature extraction Mice Classification algorithms Lesions

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