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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Raghuraman, D. Soniya, S. Shivani, S. IFET College of Engineering Villupuram Department of Computer Science and Engineering Tamilnadu Villupuram India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

Strong methods for its identification and mitigation are required due to the serious threat that Android malware poses to user security and privacy. Address this important issue by introducing a new method of combating Android malware via static analysis. To automate the detection process, our approach uses a hybrid deep learning architecture that combines Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). This paper presents a system that is specifically intended to target several types of malware, such as banking malware, riskware, adware, and SMS malware, while also differentiating between safe apps. Improving the model's accuracy and robustness is essential to its overall functionality. It starts our investigation by creating a balanced, well-categorized dataset that includes the many previously discussed categories. The methodology consists of preprocessing and gathering datasets, extracting features, and combining CNN and GRU components. While the GRU component processes the series of API calls and records temporal dependencies and patterns of behavior across time, the CNN component records spatial patterns inside static code representations. Using suitable loss functions and optimization strategies, we provide comprehensive insights into the CNN + GRU model's training and validation on the categorized dataset. A variety of criteria, such as accuracy, precision, recall, and F1-score, are used to assess performance for each category of malicious and benign apps. The outcomes of our thorough testing show how well our suggested approach detects Android malware, providing a viable way to improve device security in the face of the growing threat posed by Android malware. © 2024 IEEE.

关键词： Android malware

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Deep Learning Model Efficient Lip Reading by Using Connectionist Temporal Classification Algorithm

Deep Learning Model Efficient Lip Reading by Using Connectio...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vidhya, B. Ramkumar, M.O. Sri Yazhini, K. IFET College of Engineering Department of Computer Science and Engineering Tamil Nadu Villupuram India

ISBN: (纸本)9798350374711

The technique of translating text from a person's mouth movements is said to be lipreading. The main work of lip reading is the process that is divided into two stages using old traditional techniques: prediction of a speaker's lips and producing or learning from the visual signals. Still, the end-to-end trained models that have been studied thus far only do the classification of words rather than the prediction of sentence structure. Research has revealed that humans can lipread longer sentences, highlighting the importance of character features that could decipher context in a hazy communication channel. We provide Lip Net, a product that converts a text size stream of visual segmentation into text using the CTC Algorithm, spatiotemporal convolutions. knows that not another sentence and paragraph of the lipreading model concurrently adopts the sequential description and spatiotemporal visual clues than Lip Net. To train deep neural networks of lips to recognize voice, handwriting, and other sequences, the CTC visual guide to the CTC method was used in this instance. To illustrate how data flows via a graph, you can create dataflow graphs using TensorFlow. The graph's nodes represent different mathematical procedures. Building open-source machine learning apps is made simple for data scientists by the Stream-lit Python-based tools. However, current end-to-end model research mostly concentrates on classifying words rather than predicting whole sentences. © 2024 IEEE.

关键词： Tensors

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IoT Based Weather Forecasting for Rainfall Prediction Using Intensified Long Short-Term Memory Artificial Neural Network

IoT Based Weather Forecasting for Rainfall Prediction Using ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Vigneshwaran, V. Avinash, B. Titus Casimir, A. Vijay, D. Jayakumar, D. IFET College of Engineering Department of Computer Science Engineering Tamil Nadu Villupuram India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The purpose of this study was to evaluate the possible uses of artificial neural networks (ANNs) and intensified long short-term memory (LSTM) for rainfall forecasting. The effectiveness of these weather forecasting techniques was evaluated and assigned a score. Materials and Techniques: For the sample test analysis, intensified LSTM and ANN with pre-test power (G-power) of 80% and alpha error rate (Err) of 0.05 were used. The results of the MATLAB simulations show that the ANN has an accuracy of 95.68% and the Intensified LSTM has an accuracy of 87.99%. We discover that the achieved accuracy ratio is 0.034 (p 0.05) using the statistical application SPSS. According to this study, when it comes to advanced weather forecasting, the ANN algorithm outperforms the Intensified LSTM technique in terms of rainfall prediction. © 2024 IEEE.

关键词： Accuracy Rain Error analysis Weather forecasting Signal processing algorithms Signal processing Prediction algorithms

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Predicting Mortgage-Backed Securities Prepayment Risk Using Machine Learning Models 2

Predicting Mortgage-Backed Securities Prepayment Risk Using ...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Kanimozhi, P. Parkavi, S. Kumar, T. Ananth IFET College of Engineering Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350348002

An option to prepay a portion of a mortgage debt before it matures is included. This alternative, known as mortgage prepayment, puts the bank that provided the home loan at risk since it prevents them from receiving future interest payments and complicates their refinancing options. Prepayment risk refers to the possibility that borrowers would pay off their mortgages earlier than anticipated, lowering income flows to MBS investors. Actual prepayment rates that are greater or lower than anticipated might reduce cash flows while increasing the risk of extension for investors., Predicting the mortgage-backed securities prepayment risk analysis is necessary for this project using the mortgage portfolio of 'Freddie Mac' to predict mortgage borrower prepayment behavior. Formulating the prepayment analysis issue will be feasible using machine learning methods. Additionally, it looked at the distribution of the target variable and offered ideas for enhancing the regression model. The accuracy of ridge regression was achieved at 78%, then 89% accuracy for testing data using Logistic Regression, and, with the KNN model, achieved an accuracy of 76%. A user interface that asks for input from the user to enter the information and forecasts whether the customer's mortgage will be paid off has also been created. © 2023 IEEE.

关键词： Principal component analysis

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Diagnosis of Diabetic Retinopathy by using EfficientNet-B7 CNN Architecture in Deep Learning

Diagnosis of Diabetic Retinopathy by using EfficientNet-B7 C...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Raghu Raman, D. Nishanthi, S. Babysha, P. Computer Science & Engineering Ifet College of Engineering Villupuram India

ISBN: (纸本)9798350333602

This research study intends to assess the effects of diabetes on the thickness of each inner, outer, and peripheral retinal layer. For a very long time, it was believed that diabetic retinopathy (DR) was predominantly a retinal microvascular condition brought on by the immediate consequences of hyperglycemia and the metabolic pathways it activates. However, some current research has shown that retinal neurodegeneration, which is caused by an unfavorable ratio of neurotoxic to neuroprotective components, exists even before the onset of clinically observable microvascular damage. One such retinal illness that affects persons with long-term diabetes is diabetic retinopathy. A sign of diabetic retinopathy is the development of abnormal blood vessels within the retina. Damage to the vascular system in the eyes is a result of diabetes. Although there may not be any symptoms initially, visual issues may gradually become apparent. The pro evaluation of the physiological changes taking place within the human body is one of the most critical issues in bioengineering. Due to the numerous complexities involved in the process, detecting abnormalities in the human eye is very challenging. By 2050, 16.0 million Americans will be diagnosed with diabetic retinopathy, according to estimates. Most conventional methods for identifying diseases from retinal pictures rely on manual work. Due to the significant error rate of human observation, these strategies have a less success rate. The suggested model additionally considered the dataset's substantial class imbalance. Aptos2019-diabetic-retinopathyoversampled-256 × 256 dataset was used to train the CNN model. In order to automatically detect diabetic retinopathy, this study developed a deep learning technique called the EfficientNet-B7 model. To determine the ideal parameters to employ, hyperparameter tweaking was done. We evaluated the Efficientnet-B7 model employing an augmentation strategy that can reduce the occurrence of overfitti

关键词： Convolutional neural networks

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Cloud Storage and Secure File Sharing Using Blockchain with Xchacha20 and Shake Cryptography 16

Cloud Storage and Secure File Sharing Using Blockchain with ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Jayachitra, J. Arshad, H. Muthukumaran, R. Ifet College of Engineering Villupuram India Information Technology Ifet College of Engineering Villupuram India Artificial Intelligence and Data Science Ifet College of Engineering Villupuram India

ISBN: (纸本)9798331505264

Blockchain technology revolutionized record keeping for secure transactions but is not naturally file storage-oriented. This means that to share files securely and without central control systems, their secured decentralized sharing system needs to be developed. we propose an efficient file-sharing and storage system based on cryptographic hash function SHAKE-256 and end-to-end encryption using XChaCha20. The system highlights emphasis on privacy and integrity of data based on flexible access control policies for authorized users to access specific groups of data. Architecture will eliminate dependence on central storage solutions that are prime to vulnerabilities and single points of failure. Our solution offers a scalable and secure environment for applications in need of decentralized file storage by combining blockchain with more enhanced cryptographic techniques. Experimental results validate that the system is effective in keeping data confidential, integrity, and accessibility so it is eligible for privacy-sensitive use cases. © 2024 IEEE.

关键词： Blockchain Cryptography Decentralized Storage Secure File Sharing SHAKE-256 XChaCha20

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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

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computer Modeling in engineering & sciences 2025年第4期143卷 225-254页

作者： Aishwarya Rajasekar Vetriselvi Vetrian Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India

The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.

关键词： Internet of vehicles vehicular ad-hoc networks(VANET) multiaccess edge computing task offloading graph neural networks differential privacy

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Energy optimization mechanism using metastable-smart grid (MSSG) 4

Energy optimization mechanism using metastable-smart grid (M...

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4th National Conference on Communication Systems, NCOCS 2022

作者： Kannan, S. Pugazhendiran, P. Rishi Keshav, N. Arul Deva, A. Suresh Kumar, K. Ananth Kumar, T. Electrical and Electronics Engineering IFET College of Engineering Tamilnadu India Electronics and Communication Engineering IFET College of Engineering Tamilnadu India Computer Science and Engineering IFET College of Engineering Tamilnadu India

The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue their sustainability goals since they have control over the production, delivery, and consumption of power. Super-twisting provides a dependable method and an effective instrument for the control of uncertain nonlinear systems by addressing the basic flaws of conventional sliding mode control, notably large control effort and chattering. Fractional order controllers provide greater design freedom than conventional integer order controllers. The purpose of this work was to create a metastable-smart grid(MSSG) user end model that could dynamically manage and optimize energy produced by renewable energy sources and the utility to ensure that customers always have access to electricity. Controlling these many power sources using MSSG and delivering energy in accordance with each user's unique power plan is the responsibility of the central control unit of the distribution grid. The supply is only switched to solar by the central control unit if a user's power consumption falls below a predetermined threshold. Giving consumers with less purchasing power more clout as a result. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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