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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Detection of Skin Cancer Using SFLA Based Apex Component Analysis Network

Detection of Skin Cancer Using SFLA Based Apex Component Ana...

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

作者： G N Keshava Murthy Piyush Kumar Pareek Jagadeesh B N Nalini B M Keerthi Kumar M Rashmi P Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru India Department of 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 Department of Electronics and Communication Engineering GSSS Institute of Engineering and Technology for Women Mysuru India Inventuriz Labs Private Limited Bengaluru India

Diagnosing skin lesions using medical image analysis is still difficult. There are many different types of skin cancer, but skin lesions are quite prevalent. the most cutting-edge tools for identifying skin cancer is dermoscopy. However, a high number of training samples is often required for Deep learning-based approaches. When there are few samples available, it is challenging for a deep learning model to produce a representative representation of features for detecting skin lesions. In this work, we present an optimised simple deep learning model (OSDL) that can improve accuracy even when there is a scarcity of training data. In OSDL, the network is built using samples of skin cancer images and their innate features. The resultant model would then be able to create more robust feature expression with fewer data points. The suggested model, dubbed SFLA-OSDL, employs the Shuffled Frog Leaping Algorithm (SFLA) to fine-tune OSDL's hyper-parameters. For the purpose of deep feature extraction from skin cancer pictures, we have developed a novel network we call the Apex Component Analysis Network (ACAN). On the benchmark dataset ISBI-2016, the provided technique is exposed to outperform the high-tech tactics in relationships of accuracy and speed.

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Aquila Optimization Algorithm with Random Forest for Real-Time Fraud Detection in Financial Transactions

Aquila Optimization Algorithm with Random Forest for Real-Ti...

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

作者： Battu Durga Bhavani Komuravelly Sudheer Kumar Shilpa Ajay Zaid Ajzan Balassem Boddu Rajasekhar Department of IT Malla Reddy Engineering College for Women(RH) Hyderabad India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of Management Studies Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, there has been a significant rise in the number of credit card bank transactions as well as the frequency of fraud and card theft. However, as financial crime becomes more challenging task, so improved real-time detection methods is required. This research investigates the use of Machine Learning (ML) approach to identify fraudulent behavior in financial transactions, in real-time applications. Hence, the Aquila Optimization Algorithm with Random Forest (AOA-RF) is recommended in this research as a real-time solution for identifying financial transaction fraud. By using real-time processing, reduction of data, and ML flexibility are required when fraud tendencies arise to properly detect data patterns. By optimizing characteristics and model parameters, the combination of AOA and RF increases real-time financial transaction fraud detection. This hybrid strategy enhances forecast accuracy, decreases false positives, and dynamically adjusts fraud patterns in financial data. According to the results it illustrates that the proposed AOA-RF algorithm have achieved better performances in terms Accuracy of 99.97%, Sensitivity of 99.15% and specificity of 99.54% when compared to existing models such as eXtreme Gradient Boosting (XGBoost), Federated Learning (FL) and Stochastic Gradient Boosting (SGB).

关键词： Radio frequency Accuracy Sensitivity Heuristic algorithms Stochastic processes Boosting Real-time systems Fraud Random forests Optimization

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Revolutionizing Video Event Detection: An Efficient Approach Utilizing Temporal Convolutional Neural Networks Using Real-time Performance

Revolutionizing Video Event Detection: An Efficient Approach...

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

作者： Laith H. Alzubaidi Bura Vijay Kumar Zainab Alassedi A. H. A. Hussein Rajesh N The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its ambiguous and complex nature. A key challenge with manual detection and sequential analysis lies in the fact that video analysis often demands an immense amount of time and resources. To address this challenge, we have adopted an approach that involved Temporal Convolutional Neural Networks (TCNNs) and Long Short-Term Memory (LSTM) methods. This methodology offers a more efficient way to detect activities in video data. Furthermore, our approach enhances the capture of contextual information within the video, leading to improved results in video content detection. TCNNs, in particular, stand out as they can process input sequences in parallel, resulting in faster training and inference when compared to Recurrent Neural Networks (RNNs) which process data sequentially. To evaluate the effectiveness of these models, we conducted experiments on a manually annotated dataset known as UCF101, which contains 101 different activities. This dataset comprises 1,200 videos representing various actions. Our experimental results demonstrate that TCNNs achieved an accuracy rate of 93.80%, surpassing other existing models are ResNet and Multiclass Support vector machine in overall performance. TCNN, in particular, exhibited exceptional accuracy, indicating its effectiveness in video event detection.

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A Pricing and Inventory Optimization Control Using Integer L-Shaped Method

A Pricing and Inventory Optimization Control Using Integer L...

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

作者： Layth Hussein Ramya Srikanteswara V. Malathy Akshaya Kumar Mohanty Savitha SavithaG Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE SR University Warangal Telangana India Department of Economics School of Economics Management Information Sciences Mizoram University Mizoram India Department of CSE RV Institute of Technology and Management (RVITM) Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The optimization of price and inventory control for traditional and direct supply chain with retailer and retailer manufacture stochastic demand. The manufacturer and retailer play an Stackelberg game, the manufacture acts leader and retailer acts follower. A manufacturer decides the selling price and wholesale price in traditional channel, the retailer order the products at low price and sell through traditional channel. The demand is depended upon the retail price, wholesale price, and related prices. The retailer and manufacturer profits can be increased unexpectedly, due to demand changes in traditional channel. An optimization solution can solve the dynamic pricing, inventory decisions, and order quantity to increase expected profits. An integer L-shaped method is optimization technique, to solve problem of manufacturer and retailer in forecasting the demand in traditional channel. The integer L -shaped method achieved the $\mathbf{5 5 3 7. 0 \%}$ RDP and demonstrate the value of optimization compared to MOGWO and CVaR method in decision making. The results provide insights into how the optimal pricing and inventory policies depend on the demand parameters and channel characteristics.

关键词： Supply chain management Supply chains Decision making Optimization methods Pricing Games Data science

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Machine Learning Based Sentiment Analysis and Swarm Intelligence

Machine Learning Based Sentiment Analysis and Swarm Intellig...

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

作者： Raj Kumar Patra Bassamma Patil T. Sathish Kumar Gandla Shivakanth Manjula B M Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Electronics & Communication Engg H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. I

关键词： Sentiment analysis Sensitivity Social networking (online) Simulation User-generated content Stars Machine learning

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Mayfly Optimization Algorithm with Bidirectional Long-Short Term Memory for Intrusion Detection System in Internet of Things

Mayfly Optimization Algorithm with Bidirectional Long-Short ...

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

作者： Sanjaikanth E Vadakkethil Kiran Polimetla Zaid Alsalami Piyush Kumar Pareek Deepak Kumar Somanathan Pillai School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Dept. of Information Technology Adobe Inc. Austin Texas USA Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg Kentucky USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The rapid development of number of connected devices exchange sensitive and personal significant data through Internet of Things (IoT)-assisted global network, attacks which are targeting security services are also enhancing every day. This paper proposed a Mayfly Optimization Algorithm (MOA) with Bidirectional Long-Short Term Memory (BiLSTM) for Intrusion Detection System (IDS) in $\text{IoT}$ . The MOA is used for feature selection which select relevant features through balancing exploration and exploitation. The BiLSTM is used for classifying network into attack and normal which process the data in both forward and backward directions, allowing them to capture dependencies between events that might be far apart in sequence. The min-max normalization is used to normalize the continuous features from NSL-KDD, ToN-IoT and UNSW-NB15 datasets. The recall, accuracy, f1-score and precision are considered to evaluate MOA-BiLSTM performance. The MOA-BiLSTM realizes accuracy of 99.25%, 89.61% and 99.35% accuracy for NSL-KDD, ToN-IoT and UNSW-NB15 datasets when compared to existing techniques such as LSTM and Deep Neural Network (DNN).

关键词： Circuits Intrusion detection Bidirectional control Feature extraction Classification algorithms Internet of Things Security

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Fault Detection and Classification in Power Grids Using Shannon Entropy with Convolution Distributed Autoencoder

Fault Detection and Classification in Power Grids Using Shan...

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

作者： Vinod Veeramachaneni Saef Obad Husain Virat Khanna Piyush Kumar Pareek P. Bhuvaneshwari Department of Information Technology Colorado Technical University Springs Colorado USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq University Centre for Research & Development Chandigarh University Mohali India Department of AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Nowadays, Power grids have undergone significant transformations as the demand on electricity increased tremendously. Power grids are the primary sources for electricity generation and power distribution to the consumers need, but it faces numerous faults during this process. The traditional approaches for detection of faults in power grids had faced challenges, that includes accurately detecting and classifying which type of faults in the power grid. Therefore, this research proposes Shannon Entropy-Convolution Distributed Autoencoder (SE-CDAE) for fault detection and classification in power grids. The proposed method employed on Power Grid dataset which provides the current signals data that includes normal current signals and fault current signals. Then, the gathered data is pre-processed by fast wavelet transform which converts the time-domain signals into wavelet coefficient by removing the noise and dimensionality. These pre-processed data optimal features are extracted by using SE that effectively eliminates the irregular patterns in data. Finally, CDAE is employed for detecting and classifying the faults from extracted optimal features. The proposed SE-CDAE effectively detected and classified the faults in power grids and succeeded with high results including accuracy (97.98%), precision (94.83%), recall (96.28%), F1-score (97.35%) when compared with existing Semi-ProtoPNet Visual Geometry Group-19 (SPPN-VGG-19).

关键词： Convolution Fault detection Electricity Autoencoders Noise Feature extraction Power grids Time-domain analysis Wavelet coefficients Fault currents

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Supervised Machine Learning Based Land Cover Classification Using Sentinel Satellite Imagery

Supervised Machine Learning Based Land Cover Classification ...

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

作者： Y. Nagendar Zaid Alsalami Megha Kulkarni Abhijeet Das Kay Hooi Keoy School of Computer Science and Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar Odisha India UCSI Graduate Business School UCSI University Kuala Lumpur Malaysia

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

It is crucial to have accurate and updated cover maps for the producing sustainable crops and managing land efficiently. However, developing an accurate Land Cover (LC) mapping for efficient land management is still a major challenge in environment modeling and for agricultural regions with various landscapes that are complicated. To address this, a supervised Machine Learning (ML) based LC classification i.e., Support Vector Machine based LC (SVM-LC) is proposed which used Sentinel Satellite Imagery (Sentinel-2) dataset. The proposed research focuses on using the two parameters of SVM parameters: gamma and penalty, for identifying various crops and to classify LC. Initially, the dataset is preprocessed using Normalized Difference Vegetable Index (NDVI) and Land Surface Water Index (LSWI) followed by extracting features from, Histogram of Oriented Gradient (HOG), Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and haralick texture features. The extracted features are then fed to the optimized SVM for choosing optimal parameters and classifying them for better LC classification. The results obtained from the proposed SVM-LC has shown better LC classification accuracy with 99.43% and precision with 97.46%. The results demonstrated the efficiency and applicability of the proposed approach for efficient land management in agricultural landscapes.

关键词： Support vector machines Histograms Accuracy Land surface Crops Machine learning Feature extraction Vectors Satellite images Indexes

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Enhanced Security in Cloud Storage Systems Based on Adaptive Thresholding with Dual Attention Neural Networks

Enhanced Security in Cloud Storage Systems Based on Adaptive...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： K Venkata Naganjaneyulu Raed H.C. Alfilh Smitha Sharath Shankar S. Senthil Kumar V. Malathy School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Refrigeration and Air Conditioning Techniques 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 Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering SR University Warangal India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Recent days, cloud storage has revolutionized data management system by providing a flexible, on demand outsourcing solution that eliminated storage management complexities, reduced capital expenditures on hardware and software, which minimized maintenance requirements. Traditional approaches for enhanced security in cloud storage systems had faced significant challenges, which include increased complexity and compatibility issues. Therefore, this paper proposes Adaptive Thresholding-Dual Attention Neural Networks (AT-DANN) which is employed on Benchmarking Evaluation Toolkit for Hypervisor (BETH) dataset. This dataset is preprocessed by using secure Deduplication system, which optimize storage efficiency. Then, content-based chunking and fingerprinting which effectively breaks the data into smaller chunks. After that, proposed AT-DANN is used to adjust threshold values based on data distribution and variability and focused on relevant data features and contexts. This context are hashed by using Multiple Hashing to ensure data consistency and finally, Cross domain authentication is utilized for enabling secure data sharing between different organizations. the proposed AT-DNN for enhanced security in cloud storage systems achieved better results accuracy ratio (89 %), performance ratio (75%), encryption ratio (80 %), efficiency ratio (90 %), evaluation ratio (75 %) when compared with existing Convolutional Neural Networks (CNN).

关键词： Cloud computing Adaptive systems Neural networks Authentication Organizations Fingerprint recognition Complexity theory Security Convolutional neural networks Secure storage

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Hybrid Deep Learning Model for Spectrum Sensing Classification in Cognitive Radio Networks

Hybrid Deep Learning Model for Spectrum Sensing Classificati...

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

作者： S. Padmakala Zaid Alsalami Supriya R Sahana MP Palakurthy Deepthi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Dayananadha Sagar College of Engineering Bangalore India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Spectrum sensing (SS) uses cognitive radio technology at base transceiver stations in order to discover the licensed spectra used by Primary Users (PUs). This enables the SUs to make wireless network transmissions without causing interference. Cognitive Radio Networks (CRNs) help in the problem on the restricted radio frequency spectrum that wireless devices depend on. For enhanced classification of SS in CRN, the proposed research focused on using hybrid Deep Learning (DL) model by integrating Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Initially, the data is generated through eight modulation types considering the varied oversampling rate. The spatial features of signal are extracted through CNN and temporal features are identified by LSTM. the Cooperative Spectrum Sensing (CSS) algorithm is integrated increase the reliability of spectrum detection. The hyper parameters of the trained model are fine tuned for enhanced classification of SS. Experimental results showed that the proposed LSTM-CNN-CSS achieved sensing accuracy at 98.34%, precision at 95.62%, recall at 96.77% when compared to the traditional SS classification models such as Ensemble Convolutional Recurrent Neural Network (ECRNN) and LSTM-Extreme Learning Machine (LSTM-ELM).

关键词： Cooperative communication Modulation Feature extraction Data models Sensors Convolutional neural networks Cognitive radio Reliability Long short term memory Radio spectrum management

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