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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Sandya, Vooradi Baligeri, Veeresh lal, Bechoo Petli, Vishwanath Pradeep Kumar, S. Cmr Technical Campus Department of Cse Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India H. K. E. Society's S. L. N. College of Engineering Department of Electronics and Communication Engineering Raichur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Classification, preprocessing, feature extraction, and segmentation are all parts of the planned study that will be utilized to categories and detect brain tumor pictures. Magnetic resonance imaging (MRI) gives direct information about anatomical structures as well as possibly abnormal tissues where patients are being watched by physicians, making brain tumor identification not as much simpler for clinical diagnosis. This suggested system utilizes a machine learning strategy to identify, and categories brain tumors known as gliomas. Kirsch's edge detected pixels are used to identify the edges of the boundaries. Using this improved brain scan, the ridge let transform is used to extract the ridge let multi-resolution coefficients. As an added step, the ridge let converted coefficients are used to create features, which are then improved with the help of the CANFES classifier. Evaluation factors like as sensitivity, specificity, and accuracy are applied to the results in the context of tumor detection. Both the old approach and the suggested methodology are implemented in simulation using a programming environment like MATlAB, and the results of these simulations are compared to demonstrate the efficacy of the proposed algorithm. The suggested tumor detection approaches employing Co-Active Adaptive Neuro-Fuzzy Expert System Classifier have an accuracy of 98.73%, which offers iv accurate detection of the tumor, and so should be regarded as superior to the current traditional procedures. © 2023 IEEE.

关键词： Tumors

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A Novel Framework for Classifying Remote Sensing Images using Convolutional Neural Networks

A Novel Framework for Classifying Remote Sensing Images usin...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Manoharan, Thiyagarajan Basha, Shaik Hedayath Murugan, J.Senthil Suja, G.P. Rajkumar, R. Srimathi, S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India R.M.K. College of Engineering and Technology Department of Ece India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Avadi Chennai India Muslim Arts College Thiruvithancode Department of Computer Science Kanyakumari629174 India Rajarajeswari College of Engineering Computer Science and Engineering Mysore Road Bengaluru74 India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350359688

The classification of Hyperspectral Remote Sensing Images (HRSIs) using Convolutional Neural Networks (CNNs) has gained significant. due to excessive dimensional data and a lack of sample training examples, the Hughes phenomenon is frequently seen in hyperspectral remote sensing photos. In the realm of high-dimensional information processing, it is worth noting that there is a notable consumption of time and computing power. Additionally, there is a possibility that the features extracted may not accurately represent the data, leading to suboptimal classification efficiency and accuracy. In this research, a novel framework called GCNN is proposed, which combines the concepts of global context spatial attention (GCSA) and three-dimensional convolutional neural network (3dCNN). The GCNN framework aims to enhance the performance of spatial attention and convolutional neural networks by leveraging global context information. By integrating GCSA and 3dCNN, GCNN offers a promising approach for various applications in computer vision and image analysis. The mix-up operation is a commonly employed technique in the field of remote sensing image analysis. Its primary purpose is the spatially heterogeneous data available in these photos, and improve it. This is achieved by transforming the discrete sample space into a continuous one, thereby promoting improved flatness in the local vicinity of the data space. The integration of GCSA into the network allows for the encoding of contextual information from remote sensing scene images into the local features. The findings of this study indicate that the utilization of CNN for remote sensing image recognition yields significantly higher accuracy compared to traditional image recognition models. The recognition accuracy achieved by CNN in this context is reported to be as high as 97%. © 2024 IEEE.

关键词： Convolutional neural networks

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Understanding the Implications of SIM Card Swap Fraud in India: A Comprehensive Study

Understanding the Implications of SIM Card Swap Fraud in Ind...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bhavana, S.U. doreen Hephzibah Miriam, d. Robin, C.R. Rene S.D.N.B Vaishnav College For Women Pg Department Of Computer Science Chennai India Chennai India Sri Sairam Engineering College Department of CSE Chennai India

ISBN: (纸本)9798350352689

SIM swapping is a type of cyber attack where an attacker persuades a mobile service provider to transfer a victim's phone number to an attacker-controlled SIM card. This allows the attacker to access the victim's internet accounts, including banking and social media, by changing passwords associated with the victim's phone number. Recent high-profile incidents, such as a Californian man stealing $5.3 million in cryptocurrencies and a Massachusetts man stealing $550,000 worth of cryptocurrencies through SIM swapping attacks, underscore the severity of the crime and the urgent need for enhanced security measures to prevent such attacks. SIM swapping poses a significant security risk with potentially catastrophic consequences for victims. It is crucial for individuals to be aware of the dangers of SIM swapping and take precautions to protect themselves, such as adopting two-factor authentication and using secure passwords. Additionally, mobile service providers must implement measures to ensure the security of client accounts against SIM swapping attacks. despite experiencing numerous intrusions, many people in India remain unaware of effective cybersecurity solutions to safeguard their businesses, leading to significant losses. This article discusses the process of SIM card swapping, its benefits and drawbacks, real-life instances of SIM swapping attacks, and its impact on cybersecurity. It also provides several preventive measures to mitigate the risk of similar attacks. © 2024 IEEE.

关键词： Cybersecurity

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The Application of Machine learning Algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Gurumoorthy, Sasikumar Avikkal, Saritha Rani, M. Radha, K. Rajesh, N. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (AdHd). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of Ml models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Electroencephalography

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An Investigation on Machine learning Models in Classification and Identifications Cervical Cancer Using MRI Scan

An Investigation on Machine Learning Models in Classificatio...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Singh, Jagendra Tan, Kuan Tak Sahu, dinesh Upreti, Kamal Kshirsagar, Pravin R. Elangovan, Muniyandy Singapore Institute of Technology 10 Dover Drive Singapore School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India J D College of Engineering & Management Deptartment of Electronics & Telecom. Engineering Nagpur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India

ISBN: (纸本)9798350394399

This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. during the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. © 2024 IEEE.

关键词： Classification (of information)

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Advanced IoT and Machine learning Solutions for Sustainable Groundwater Management Using Edge-Based Residual Graph Attention Network Model 2

Advanced IoT and Machine Learning Solutions for Sustainable ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mrudula, J. Praveen, R.V.S. Nithiyanantham, P. Sowmya, Vemula Jasmine Shinde, Nitin Nivrutti Ganvir, Priyadarshan Sham Geethanjali College of Engineering and Technology Department of Electronics and Communications Engineering Cheeryal Telangana Hyderabad India Digital Engineering & Assurance Sarelingampalli Telangana Hyderabad India Nandha Engineering College Department of Biomedical Engineering Tamil Nadu Erode India KLEF Department of Computer Science & Engineering Andhra Pradesh India Dr.D.Y.Patil Institute of Technology Department of Civil Engineering PimpriMaharashtra Pune India Mahatma Gandhi Arts Science and Late N. P. Commerce College Department of Geology Maharashtra Armori India

ISBN: (纸本)9798331534950

The sustainability of ecosystems and human resilience to changing and unpredictable climates are both impacted by groundwater, the world's biggest distributed freshwater storage system. Groundwater supplies can shift, nevertheless, due to abstraction, changes in land use, and climate change. Many traditional methodologies, such as targeted numerical approaches, have been suggested for groundwater modelling as potential answers to these problems. Preprocessing, feature selection, and training the model are the three stages that make up the suggested method. In pre-processing, the proposed approach addressed the null values, sparsity problem, and missing values to get the data ready for testing. In this stage, the proposed system apply a feature selection strategy that combines KM clustering with CFS ranking to deal with the high dimensionality of the input data. The suggested method relied on the Residual GAT for model training. Compared to state-of-the-art alternatives like GAT and lSTM, the proposed model typically achieves a better accuracy rate of 91.62%. © 2024 IEEE.

关键词： Adversarial machine learning

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Clustering and Energy Efficient Power Allocation Scheme for d2d Multicast Network 2

Clustering and Energy Efficient Power Allocation Scheme for ...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Nallasivan, G. Shajitha Banu, A. Rajesh, V. Arumuga Babu, M. Ananthan, Bhuvanesh Mahesh, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department Electronics and Communication Engineering Tamil Nadu Krishnankoil India Christ the King Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350383652

The multimedia content dissemination services with the current cellular networks still met several challenging while performing desired performance. Recently, the improvement of energy efficiency is not considered in most of the existing works and for all receiving users, they discussed about how to deliver reasonable QoS. Based on multicast network, this work proposed a novel framework to solve resource allocation as well as clustering in device-to-device (d2d). This study introduce a novel adaptive energy valley optimization (AEVO) model to enhance the clustered and power allocation with energy efficient. While considering the requirement of QoS in d2d, an efficient power control mechanism and resource allocation is maximized complete d2d multicast clusters energy-efficiency. By doing this, higher computational difficulties with energy-efficiency problems are solved by proposing novel allocation of power model. Based on the existing methods, the proposed algorithm indicated good numerical results with respect to energy consumption and throughput. © 2024 IEEE.

关键词： d2d multicast network and Adaptive energy valley optimization Energy efficiency Power allocation QoS

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Human Action Recognition Using difference of Gaussian and difference of Wavelet

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Big data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara deepika lakshmanan Malliga duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a difference of Gaussian(doG)filter to extract scale-invariant features and a difference of Wavelet(doW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the discriminant of Guassian(doG)and difference of Wavelet(doW)features are *** discriminant Analysis(ldA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of doG+doW is observed as 83.6635%while the average accuracy of discrinanat of Guassian(doG)and difference of Wavelet(doW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation doG+doW is observed as 62.5231%while the average accuracy of difference of Guassian(doG)and difference of Wavelet(doW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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leveraging differently layered Convolutional Neural Network for Speech Emotion Recognition

Leveraging Differently Layered Convolutional Neural Network ...

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International Conference on Advances and Applications of Artificial Intelligence and Machine learning, ICAAAIMl 2023

作者： Gaddam, Prathik Kumar Iqubal, Anas Hasan, Muqtada Agrawal, Arun Prakash School of Engineering & Applied Science The George Washington University Washington D.C United States Galgotias College of Engineering and Technology Greater Noida India Kratikal Tech Private Limited Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819795062

Text stands to be one of the prominent aspects of Communication in any Technical Phenomenon. But, with Text, understanding the Emotion of a Person, which plays a vital role in decision-making for actuating a Task, isn’t possible. With the Escalated Indulgence of Speech into diverse Technological Systems, it has been necessary to formulate a modus operandi which would precisely determine the Emotion of a Person through its Speech. different approaches have been proposed for recognizing the Emotion of a Person through Speech, but the limitation of Modular Outlook over Speech data seems to be Visible. In this paper, we consummated Convolutional Neural Networks and tried to understand the Impact of the Convolution layers, i.e., 03, 04, 05, and 06—layered CNN, over the Speech data and thereby formulate a Potent and Efficient Solution for Speech Emotion Recognition. We utilized the well-known Open-Source datasets such as CREMA, RAVdESS, SAVEE, and TESS, consisting of Speech Audio for various Human Emotions, and combined them into one whole dataset. The outcome we got was phenomenal as the Accuracy surpassed the already existing methodologies, and also it gave us an Impactful Elucidation of its Real-time Applicability for varied use-cases related to Speech. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Convolutional neural networks

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Comparative Impact Assessement of Energy Storage Systems on Frequency Regulation with Various Operating Strategies under ABT

Comparative Impact Assessement of Energy Storage Systems on ...

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Smart and Sustainable developments in Electrical engineering (SSdEE), IEEE International Conference on

作者： B.N. Suthar V.M. dholakiya Electrical Engineering Department L.E. College Morbi Gujarat India Electrical Engineering Department L. D. college of Engineering Gujarat India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper investigates the comparative impact assessment of energy storage systems on frequency regulation with various operating strategies under Availability Based Tariff in an isolated power system having generation from thermal, wind and wave energy sources along with various energy storage systems(ESS) — Superconducting Magnetic Energy Storage (SMES), Supercapacitor Energy Storage (SCES), Redox Flow Battery (RFB), and Flywheel Energy Storage — for mitigating frequency deviations. Simulation studies in MATlAB/Simulink were conducted to evaluate key performance under dynamic operating conditions. Results reveal that SMES and RFB exhibit relatively better response with minimal overshoot (≤ 0.001 Hz), negligible undershoot, and least instantaneous settling time, making them appropriate for rapid frequency stabilization. SCES also demonstrates effective performance, albeit with slight oscillations due to its slower discharge rate. The analysis emphasizes the critical role of ESS technologies in enhancing the frequency regulation under different operating strategies under ABT. This study provides insights into the preliminary selection and integration of ESS in modern power systems, contributing to the reliable and stable grid operations amidst increasing renewable energy penetration.

关键词： Wind energy generation Time-frequency analysis Wind energy Tariffs Power system stability Wind power generation Iron Frequency control Oscillators Energy storage

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