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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Karthika, S. Priyanka, T. Indirapriyadharshini, J. Sadesh, S. Rajeshkumar, G. Rajesh Kanna, P. Intelligence and Machine Learning Bannari Amman Institue of Technology Department of Artificial Sathyamangalam India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Velalar College of Engineering and Technology Department of Ai and Ds Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350300888

Weather forecasting has a greater effect on human life, recently this filed has gained a significant research attention. The existing numerical weather prediction models are very complex to solve. Therefore, the primary goal of this research work is to create a novel and lightweight weather forecasting model. Here, the time series data have been used from Kaggle website. The obtained dataset is then processed with the help of deep learning techniques. The Long Short-Term Memory (LSTM) algorithm is used to produce better result with more accuracy when compared with other deep learning methodologies. Therefore, from the results it is evident that the proposed model can be used to predict the weather conditions with greater accuracy and assist farmers and others who depend on weather. © 2023 IEEE.

关键词： Long short-term memory

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Heart Disease Prediction Model using various Supervised Learning Algorithm 12

Heart Disease Prediction Model using various Supervised Lear...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Upadhyay, Sachin Dwivedi, Anjali Verma, Ashutosh Tiwari, Vatsya Gla University Department of Computer Engineering & Application Mathura India Government Girls Polytechnic Computer Science and Engineering Amethi India G.L. Bajaj Group of Institutions Computer Science & Engineering Mathura India Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India

ISBN: (纸本)9781665462617

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning's core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87). © 2023 IEEE.

关键词： Support vector machines

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CNN supported automated recognition of Covid-19 infection in chest X-ray images

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Materials Today: Proceedings 2022年 66卷 1201-1210页

作者： Padmakala, S. Revathy, S. Vijayalakshmi, K. Mathankumar, M. Department of Computer Science and Engineering St.Joseph's Insitute of Technology Chennai India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient's sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%. © 2022

关键词： Coronavirus

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Multispectral image processing using ML based classification approaches in satellite images

Multispectral image processing using ML based classification...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tallapragada, V. V. Satyanarayana Venkatesan, G. Manisha, G. Sivakumar, N. Kumar, Ashok Karthika, J. Department of Electronics and Communication Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Civil Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9781032669663

Many academics now choose to use intelligent algorithms over traditional ones when dealing with huge amounts of data, thanks to the introduction of technologies that resemble nature. Among these algorithms, the generative adversarial network (GAN) is one of the most important. The purpose of this research is to investigate whether the GAN can be used to classify multispectral images captured by satellites in the research area. Agriculture is represented by the research area. In addition to researching the GAN’s capability to classify multispectral images, an architecture for this domain is being developed. When the GAN was used to classify data, it showed higher accuracy compared to Maximum Likelihood, where the GAN had an overall accuracy of (84.14%), while Maximum Likelihood had an accuracy of (81.66%). © 2024 The Author(s).

关键词： Generative adversarial networks

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Novel optimal region based robust watermarking using modified dragonfly optimization algorithm

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International Journal of Information technology (Singapore) 2025年第2期17卷 851-858页

作者： Shamna, N.V. Kaur, Puneet Raghavendra Patil, G.E. Krishna, Nanditha P A College of Engineering Dept of CSE (IoT & amp Cyber Security including Blockchain Technology) Karnataka India EEE UIET Panjab University Chandigarh India Department of Computer Science and Engineering Faculty of Engineering and Technology Jain Deemed-to be University Karnataka Bengaluru 562112 India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

The growing concern about unauthorized access and tampering with digital images requires the development of robust and secure watermarking methods. Traditional approaches frequently struggle with achieving a balance between security, image quality, and watermark strength, especially in high-resolution images. This study addresses these issues by proposing a new watermarking technique that combines the Blowfish encryption algorithm, the Discrete Wavelet Transform (DWT), and the Modified dragonfly optimization algorithm (MDFOA). The proposed approach begins with the Blowfish algorithm encrypting the secret image, providing increased security even if the watermark is detected. High-frequency features are preserved by embedding the encrypted watermark within the LL subband of the cover image, which is generated using DWT decomposition. MDFOA is used to identify the best embedding places inside the LL subband, which improves the watermark's concealment and resilience. The same methodology is used to repeat the extraction process, providing reliable and secure data transmission throughout the process. The proposed method is a highly effective solution for secure and high-quality image watermarking. It outperforms existing techniques in terms of Peak Signal-to-Noise ratio (PSNR), Normalized correlation (NC), Bit error rate (BER), and Normalized absolute error (NAE), according to extensive experiments conducted on a variety of high-resolution images. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blowfish encryption algorithm Discrete wavelet transform Modified dragonfly optimization algorithm Watermarking technique

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Advancing Cloud Security Frameworks Implementing Distributed Ledger technology for Robust Data Protection and Decentralized Security Management in Cloud Computing Environments 2

Advancing Cloud Security Frameworks Implementing Distributed...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Rajasekar, P. Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Pandian, A. Pasumpon Department of ECE Sri Krishna College of Technology Kovaipudur Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350383867

Cloud computing caused the paradigm shift in business performance, from scalability to flexibility as well as affordability in the management of computing resources. Nevertheless, ensuring security of the data remains a key issue in the cloud due to the centralized interactions between service provider and a single vulnerability in the network can put rest of the data at risk. Consequently, the application of DLT, in addition to other solutions such as DLT, demonstrates a big step towards the improvement of cloud security frameworks. This article focuses on the implementation of DLT for data protection applications that are based on a solid dataset and decentralized security procedures in cloud environments. DLT utilization, like blockchain, provides an organization with integrity data, transparency, and fitness to counter cyber threats. Architecture stacks up sponsors such as cloud providers, users, and applications and DLT is responsible for the security of the transaction and the security policy enforcement. Customers use those services via desktop based application with transparent security policy arisen from DLT. Moreover, DLT also does identity management in the way that is decentralized, data sharing in the manner that it is secure, and the compliance to regulatory frameworks, which in addition reduces the trust and privacy risks in the cloud ecosystems. The proposed architecture is genuinely a big jump toward protecting cloud security from centralized control interruptions and thus, enhancing resilience and stability in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Harris Hawks Optimization Based Feature Selection on High Dimensional data 2

Harris Hawks Optimization Based Feature Selection on High Di...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Jena, Suvendu Kumar Kumar, Gotte Ranjith Al-Fatlawy, Ramy Riad Ali, S.Rasheed Mansoor Aruna, T.M. Indira Gandhi Institute of Technology Department of Computer Science Engineering and Applications Odisha Sarang India School of Computer Science & Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirappalli India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350373110

In recent times, evolutionary algorithms have been demonstrating significant benefits in feature selection due to their simplicity and global search capability. However, most of the existing evolutionary algorithms are computationally intensive for high-dimensional data. In order to decrease computational costs, effective evaluation methods are crucial. In this paper, Harris Hawks Optimization (HHO) technique is proposed for effective feature selection on high dimensional data. Firstly, a Multi-Layer Perceptron (MLP) is used to form an integer optimization problem for efficient feature selection process. Then the proposed HHO method is applied for the process of feature selection. This method has three steps such as Initialization, solutions update and classification. The performance of proposed HHO algorithm is compared with the existing classifiers such as Binary Waterwheel Plant Algorithm (BWWP A) and Community detection based Genetic Algorithm (CDGAFS) and achieved 98.2% of accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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An Effective Forest Fire Prediction based on Contrastive Vision Transformer with Pool Former 2

An Effective Forest Fire Prediction based on Contrastive Vis...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. Husain, Saef Obad Ezhil, G. Indhumathi, V. Revathi, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Humanities Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Sona College of Arts and Science Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Since years, forest fires are a significant concern globally, causing damage to ecosystems and human settlements. For effective prevention and mitigation predicting forest fires is important, different factors contribute to forest fires which includes human activities, topography, climate change and weather conditions. Traditional approaches for predicting forest fires had faced significant challenges which include limited validation of prediction models and lack of standardization. Therefore, this paper proposes Contrastive Vision Transformer (CViT)-Pool Former for predicting forest fire which is employed on Forest Fire Big Data dataset which enabled better comparability. This dataset is preprocessed by using data augmentation, noise reduction and data enhancement techniques. Then, the features are extracted by using CViT which extracted optimal features based on weather conditions and captured contextual relationships by maximizing mutual information. After that, Pool Former light weight attention module is used for predicting fires which gave more accurate predictions by enabling targeted prevention and mitigation, minimized unwanted deployments and resource waste, boosted prediction accuracy by token fusion and enhanced representation learning. The proposed CViT-Pool Former method established better results including accuracy (92.8%), precision (89.4%), recall (91.4%) and F1-score (90.2%) when compared with existing Region Proposed Convolutional Neural Network (R-CNN). © 2024 IEEE.

关键词： Premixed flames

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Efficient Model for Multiview classification for diagnosis of Brain Tumors.

Efficient Model for Multiview classification for diagnosis o...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Anil Kumar, B. Chandrakala, B.M. Shruthi, B.V. Rv Institute of Tech & Management Dept of Computer Science & Engineering Bangalore India Dayananda Sagar College of Engineering Dept of Information Science & Engineering Bengaluru India Nitte Meenakashi Institute of Technology Dept of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306088

Brain tumors are caused mainly by DNA abnormalities, which result in aberrant cell division, prolonged cell lifespans, and the creation of aggregative masses inside the brain. Such tumors prevent the brain from operating at its best, sometimes resulting in long-term problems. These tumors' genesis (primary or secondary) and malignancy (benign or malignant) are used to classify them. While some might not be immediately lethal, their growth or contact with healthy brain tissue might cause damage. Investigations into various approaches, such as conventional medical imaging and artificial intelligence (AI), mainly using deep learning (DL) and transfer learning (TL), is prompted by efforts to identify and categorize brain tumors. Implementing the EfficientNetB0 model, which is famous for its adaptability and efficacy, is an excellent demonstration of this strategy. This work uses a well-curated dataset of 3264 photos from various classes to closely analyze the use of the EfficientNetB0 TL model to identify and classify brain tumors from MRI scans. The model's adaptability and accuracy are meticulously examined using a dual-pronged strategy incorporating transfer learning and fine-tuning, yielding an astounding accuracy of 98.5%. Accuracy and loss charts illustrate the model's learning development in vivid detail, and a well-defined confusion matrix provides extensive insights into categorization effectiveness across various tumor classifications. The achievement of a 98.5% accuracy rate highlights the potential paradigm change made possible by AI and TL in the diagnosis and classification of brain tumors. This accomplishment confirms the efficacy of such sophisticated procedures and promises to speed up diagnosis, enable prompt therapies, and improve patient prognoses. The combination of AI and TL heralds a new era of precision medicine, ready to reshape the field of brain tumor care as research in this area grows. © 2023 IEEE.

关键词： Brain

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Blessie, A. Alice Sankarasubramanian, R.S. John, Joseph Krishnapriya, S. Bhuvanesh, A. Keerthanadevi, R. Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Psg Institute of Technology and Applied Research Department of Mathematics Coimbatore India Gregorian Institute of Technology Department of Mechnanical Engineering Kottayam India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites. © 2024 IEEE.

关键词： Construction industry

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