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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： G N Keshava Murthy Manjula G Harshitha P Achyutha Prasad N Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka India Department of CSE BGS College of Engineering and Technology Bangalore India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked.

关键词： Telemedicine Wireless networks Metaheuristics Watermarking Medical services Elliptic curve cryptography Discrete wavelet transforms

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Histopathology Image Analysis Utilizes Homology-Based Feature Extraction for Accurate Diagnosis of Various Lung Diseases

Histopathology Image Analysis Utilizes Homology-Based Featur...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Haider Alabdeli N Subhash Chandra Sowmya M R. Bhavani Bura Vijay Kumar The Islamic University Najaf Iraq CVR College of Engineering Telangana India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India Sathyabama Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India

Histopathology image analysis is pivotal for diagnosing lung diseases, providing critical insights into tissue abnormalities for accurate medical assessments. Despite its significance, challenges arise from interpretational subjectivity and handling diverse tissue types, limiting its widespread application. The lung and colon histopathology dataset, comprising 25,000 color images, serves as a valuable resource in comprehensive medical research, offering diverse samples for detailed analysis and diagnosis. The proposed method introduces innovation by leveraging homology-based image processing for feature extraction, integrating the Unsharp Masking method in preprocessing. Classification through a Convolutional Neural Network (CNN) ensures a thorough and accurate analysis of histopathological images in lung disease diagnosis. Comparative analysis with existing approaches like 2D wavelet features and CRCNN-net underscores the undeniable superiority of the proposed method. The results demonstrate exceptional performance metrics, with an accuracy of 98.67%, precision at 93.70%, an F1-score of 80.22%, recall reaching 89.76%, and specificity standing at 90.82%. These outcomes affirm the proposed method's efficacy in advancing the field of histopathological lung disease diagnosis.

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Optimized Convolution Neural Network Based Fake News Detection Using Sentiment Analysis

Optimized Convolution Neural Network Based Fake News Detecti...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Karthik Rajashekaran R. Archana Reddy Piyush Kumar Pareek Dipti Sontakke School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Department of Computer Science New York University New York NY USA School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In the era of social media, the social media and smartphone popularity has enhanced exponentially. By the electronic media, fake news has rising quick with new information which are hugely untrustworthy. The search engine like google are incapable for fraudulent of news because its limitation with restricted keywords. The Optimized Convolution Neural Network (OPCNN) is proposed for classifying fake news into actual and fraudulent news based on sentiment analysis. which accommodate varying complexity levels by adjusting its architecture in Fake News Detection (FND). The Principal Component Analysis (PCA) is used to extract features from preprocessed images which reduces the data dimension comprising numerous related variables and recalls the high change in real data. The ISOT dataset is preprocessed through four various techniques. The recall, accuracy, f1 score and precision with ISOT dataset are considered to evaluate OPCNN performance. The OPCNN realizes 99.58% recall, 99.67% accuracy, 99.61% f1score and 99.64% precision for ISOT dataset when matched to previous techniques like Random Forest (RF) and Sea Turtle Foraging Optimization-based Fake News Detection and Classification (STODL-FNDC).

关键词： Sentiment analysis Convolution Social networking (online) Neural networks Data preprocessing computer architecture Feature extraction

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Predictive Analysis Using Modified Inception Net and Support Vector Machine for Forecasting Copra Yield in Agriculture

Predictive Analysis Using Modified Inception Net and Support...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Kavitha M G Raviprakash M L Kassem Al-Attabi B. Sathyavani S meenakshi Sundaram Department of Computer science and engineering Kalpataru Institute of Technology Tiptur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

One of the major economic sectors in various states is copra agriculture, with a significant portion of its global production coming from countries such as India, Indonesia, and Sri Lanka. However, the severity of copra issues can vary depending on factors such as moisture levels, fungal presence, and the quality of the copra, leading to imbalanced data and potential overfitting in copra image analysis. Threshold-based segmentation is involved to identify different segments of copra based on factors like moisture content. Higher moisture content may indicate areas prone to fungal growth or poor drying conditions, while segments with uniform colour and texture may signify high-quality copra. The proposed approach involves using the Modified Inception Net (MIN) for feature extraction and Support Vector Machine (SVM) for hyperparameter tuning. SVM handles the kernel function in nonlinear and high-dimensional spaces, enhancing its capabilities and mitigating overfitting. However, the MIN-SVM combination may require significant computational resources, particularly during training and parameter tuning. The proposed MIN-SVM method achieves promising performance metrics, including a Mean Absolute Error (MAE) of 0.071, Mean Square Error (MSE) of 0.0125, Matthews Correlation Coefficient (MCC) of 0.620 and $R^{2}$ value of 0.9410. This performance is compared to existing methods such as Back Propagation Algorithm (BPA), K-means and SVM.

关键词： Support vector machines Measurement Backpropagation Image segmentation Moisture Production Feature extraction

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Self-moving Window with Long-Short Term Memory based Anomaly Detection in Edge-Assisted IoT Environments

Self-moving Window with Long-Short Term Memory based Anomaly...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yerrolla Chanti Myasar Mundher Adnan R. Renuga Devi Lakshmanan M Arelli Madhavi School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram India Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the detection of anomalies accurately which lead to risk of losing information. To overcome this limitation, a Self-moving Window with Long Short-Term Memory (SW-LSTM) is proposed for accurate detection of anomalies. The self-moving window improved the ability of LSTM to identify subtle anomalies that evolve over time, leading to more accurate detection of unusual patterns or device behavior in IoT environments. Initially, the data about attacks and malicious nodes are acquired from the UNSW-15 dataset and preprocessed by score normalization technique. Then, the optimal set of features are selected by Particle Swarm Optimization (PSO) algorithm. Finally, the proposed SW-LSTM model detects the anomalies in edge assisted IoT network effectively. The experimental results of proposed method achieved accuracy of 93.4% which is greater when compared to existing detection approaches such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN).

关键词： Accuracy Recurrent neural networks Image edge detection Computational modeling Internet of Things Convolutional neural networks Object recognition Particle swarm optimization Long short term memory Anomaly detection

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Ant Colony Optimization based Medical Image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Contrastive Loss Function Siamese Network for Change Detection on Remote Sensing Images

Contrastive Loss Function Siamese Network for Change Detecti...

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Data science and Network Security (ICDSNS), International Conference on

作者： N. Naga Saranya Ahmad Alkhayyat Shreyas A V Raviprakash M L T. Jayachandran Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of ECE Nandha Engineering College Erode India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

To observe the changes in the earth surface is increasing due to development of navigation software applications and also mainly prediction purposes for agriculture purpose and environmental hazards. To overcome the problem one of the most crucial technological choices for effectively identifying changes in observations in remote sensing photos is Change Detection (CD). Applications for CD are numerous and include mapping disasters, environmental monitoring, urban planning and land use study. On the other hand, the CD applications face significant hurdles due to the often-occurring class imbalance issue. In order to tackle this problem, a new type of Contrastive loss function Siamese network (CLFS-Net) that integrates the links between scenes and foregrounds to yield global-aware features for effective change detection. The CLFS-Net improve symbiotic connection learning between scene and foreground while simultaneously learning contextual relationships. The proposed CLFS-Network gained in terms of accuracy as 90%, precision as 95% and recall as 91% and f1 score as 95% respectively.

关键词： Symbiosis Accuracy Navigation Urban planning Network security Feature extraction Software Robustness Sensors Remote sensing

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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization

Fault Classification in Photovoltaic Arrays Using Convolutio...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Pradeep Kumar S. L. Umasankar Muntather Almusawi Pothireddy Surekha Srinivas Aluvala Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Electrical and Electronics Engineering S.A.Engineering College Poonamallee Tiruvallur India The Islamic university Najaf Iraq Department of Computer Science and Engineering GRIET Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal India

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods; and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems.

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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Mohammed I. Habelalmateen Satish Reddy Kasarla Jajimoggala Sravanthi P. Kavitha S meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Hyderabad Institute of Technology And Management Hyderabad India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN).

关键词： Support vector machines Wireless communication Accuracy Fault detection Instruments Nearest neighbor methods Feature extraction Entropy Calibration Computational efficiency

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Weed Recognition Using Image Patches Based Global Hybrid Attention with Densenet-169 Model

Weed Recognition Using Image Patches Based Global Hybrid Att...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Hima Bindu Valiveti Muntather Almusawi E G Satish Srinivas Aluvala E S Challaraj Emmanuel Department of ECE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Life Sciences Kristu Jayanti College Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Weeds are one of the main problem in agriculture that affect the crop yield. Accurate classification and recognition is major challenges in weeding because of same visual characteristics between plants and weeds. To solve this problem, in this research patch based image recognition is integrated with the Deep Learning (DL) approach to improving the classification and recognition of weeds. The weed image data are collected from the Deepweeds dataset. For pre-processing Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is utilized for change the image size and then generated to small patches. Selection of relative important patches are performed by Fast Fourier Transform (FFT) and Laplacian Filter (LF) are trained to the DL model. The proposed Global Hybrid Attention mechanism with Densenet-169 model (GHA-Densenet-169) employed for classification of weeds with respective classes. The evaluation results of the proposed method using performance metrics are Accuracy, Recall, Precision and F1-score. The proposed approach attained high accuracy of 99.28%, recall of 99.26%, precision of 99.27% and f1-score of 99.25% which is greater than existing deep learning models such as Densenet-201, VGG-SVM, Adapting MobilenetV2, Resnet-SVM, and Swin Transformer and Two-stage Transfer Learning (ST-TSTL).

关键词： Deep learning Adaptation models Visualization Image recognition Accuracy Laplace equations Fast Fourier transforms Computational modeling Feature extraction Transformers

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