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Cost Sensitive based Support Vector Machine for Energy Efficient Clustering and Routing

Cost Sensitive based Support Vector Machine for Energy Effic...

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

作者： Vijaya Bhaskar Reddy Muvva Mohammed I. Habelalmateen V Shridhar Pradeep V Manohar M Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Science and Engineering Alva's Institute of Engineering and Technology Mangalore India Department of Computer Science and Engineering CHRIST (Deemed to be University) Bangalore India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Wireless Sensor Networks (WSNs) are ideal for applications requiring rapid deployment, as they operate without the need for a pre-existing network infrastructure. WSNs have constrained capacity to manage large volumes of data, and processing, transmitting as well as receiving this data consumes substantial energy. On account of their constrained power usage and bandwidth, sensors are unable to transmit each gathered information to a Base Station (BS) or sink for analysis. Thus, this research proposes the Cost Sensitive based Support Vector Machine (CS-SVM) approach for the energy efficient clustering and routing. The proposed approach minimizes an energy consumption at data transmission and clustering, extending network lifetime through effectively selects CHs according to energy levels as well as node distribution. The CH selection considers the three important fitness functions such as distance from sink to CH, distance from CH to sensor nodes and residual energy. Then, the routing considers the two important fitness functions. The various performance indices are considered to validate the effectiveness of proposed method. The simulation outcomes proven that the proposed CS-SVM approach attains the better energy consumption of 8.32J as compared to the previous approach named Distributed Energy-efficient two-hop-based Clustering and Routing (DECR).

关键词： Support vector machines Wireless sensor networks Energy consumption Costs Routing Energy efficiency Energy states Magnetic heads Sensors Data communication

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The People Moods Analysing Using Tweets Data on Primary Things with the Help of Advanced Techniques

The People Moods Analysing Using Tweets Data on Primary Thin...

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

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Rohith Vallabhaneni Piyush Kumar Pareek Sravanthi Dontu School of Electrical Engineering and Computer Science University of North Dakota Grand Forks North Dakota USA University of the Cumberlands USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands USA

Researchers studying sentiment analysis are interested in Twitter, a major social media platform. Text mining research on Twitter Sentiment Analysis (TSA) is growing. Using computers, “TSA” processes subjective Twitter data like opinions and sentiments. Social media platforms like Twitter have many customer reviews. Traditional customer feedback methods like focus groups, business surveys, and questionnaires take time and resources. However, Twitter reviews lack structure, making manual analysis difficult and time-consuming. Using machine learning and deep learning for sentiment analysis yields poor classification accuracy. This study uses optimized deep learning and machine learning models and clever SNS scraper-extracted tweets. Experimental results show that the proposed method can classify three emotions with 99% accuracy. A hybrid classifier (HC) with LSTM and Bi-GRU classifies. When Bi-GRU and LSTM are averaged, effective output is obtained. The proposed Hosted Cuckoo Optimisation Algorithm fine-tunes LSTM weight to improve detection accuracy.

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Cybersecurity Threat Detection in Smart Cities using Box Plot Sampling Isolation Forest

Cybersecurity Threat Detection in Smart Cities using Box Plo...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： S. Uma Shankari Haeedir Mohameed Megha Kulkarni S. Aravindh N. Purushotham Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical Systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT_IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively.

关键词： Smart cities Botnet Forestry Feature extraction Threat assessment Encoding Numerical models Internet of Things computer crime Standards

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Strengthening Cybersecurity using a Hybrid Classification Model with SCO Optimization for Enhanced Network Intrusion Detection System

Strengthening Cybersecurity using a Hybrid Classification Mo...

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

Polymorphic malware and encrypted traffic hinder Network Intrusion Detection Systems (NIDS) from detecting complex attacks. Cybercriminals exploit NIDS algorithm vulnerabilities, showing how attack tactics and cybersecurity defenses change. This study suggests improving Network Intrusion Detection Systems. A thorough preprocessing phase with normalization functions improves data accuracy and consistency. The Single Candidate Optimization (SCO) feature selection algorithm optimizes NIDS efficacy. A hybrid model using Wavelet Transform, Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN) is used for classification because it can identify sequential dependencies in network traffic data. A second SCO iteration for hyperparameter tuning optimizes model performance and refines. The evaluation stage uses the BoT-IoT dataset, a prominent benchmark. SCO can improve hyperparameter optimization and feature selection to create a more accurate and cyberattack-resistant NIDS. The method's 99.6% accuracy is confirmed by experiments and performance evaluations. This shows how effective it is compared to current models, which strengthens cybersecurity defenses against changing attack landscapes. Similar trends are seen in the F1-scores, which range from 96.3% (ResNet50) to 99.4% (Proposed model). The Projected model performs exceptionally well in terms of stands out with the highest values in all metrics.

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Financial Fraudulent Detection using Vortex Search Algorithm based Efficient 1DCNN Classification

Financial Fraudulent Detection using Vortex Search Algorithm...

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

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Rohith Vallabhaneni Piyush Kumar Pareek Sravanthi Dontu School of Electrical Engineering and Computer Science University Of North Dakota Grand Forks North Dakota USA University of the Cumberlands USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands USA

Repeated loan fraud threatens financial stability and drives away clients. Financial and banking institutions must immediately recognize network fraud. The banking sector in India and other countries is using AI-driven technology and machine learning algorithms to tackle fraud and unauthorized access. Innovative thinking, increased globalization, and technical developments are raising fraud detection costs. The suggested study would help banks identify credit application fraudsters. The study automated data preprocessing using Kaggle's K-Nearest Neighbor (KNN) algorithm. A one-dimensional convolutional neural network classified. Using the Vortex Search Algorithm (VSA) to fine-tune the classifier hyperparameters improved results. VSA determined the model's hyperparameter sweet spot. The suggested model outperforms other categorization methods with 98.62% accuracy. Better lending banking fraud detection may result from the proposed approach. The VSA-based 1DCNN model detects fraud faster and more precisely.

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Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) for Cyberattack Detection in Healthcare

Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA)...

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

作者： Laith H. Alzubaidi Nijaguna G S Syed Nawaz Pasha Veeranna Kotagi K. Kalaiselvi The Islamic University Najaf Iraq Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India School of Computer Science and Artificial Intelligence SR University Warangal India Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore India

Usage of Internet of Things (IoT) in medical area is defined as IoMT that plays a signif-icant part in exchanging of sensitive information between medical systems. Though, that comes with privacy securities which comprised the security of information gathered through medical sensors, made them susceptible for potential cyber issues like modification of data, attacks re-play. These attacks can cause important information loss or unauthorized modifications. In this research, a Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) is proposed for cyberattack detection. The dataset used in research is WUSTL-EHMS-2020 which is healthcare dataset. The dataset is pre-processed through label encoding and data normalization. Then feature extraction is performed by Kernal Partial Least Square (KPLS) Method and detection is performed through GBBOA based SVM. The perfor-mance of proposed method is analysed with performance measures of accuracy, precision, re-call and f1-score. The proposed method attained accuracy 97.72%, precision 97.03%, recall 96.56% and f1-score 96.84% which is efficient than other existing methods like Particle Swarm Optimization – Machine learning (PSO – ML) and ensemble Machine Learning (ML) methods.

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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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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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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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