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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajapati, Abhinav Gupta, Ayush Mishra, Sanskar Raj, Dharm Singh, Murari Kumar Goyal, Mayank Kumar Sharda School of Engineering and Technology Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9789380544519

The exponential growth of large, complex, and dynamic datasets, known as big data, required the development of robust analytical techniques beyond conventional tools. This article examines the landscape of big data processing techniques like LSTM, CNN, and more identifying their suitability for different application domains like social media, finance, healthcare, and agriculture. Recognizing the frequent time-sensitive nature of big data analysis, we emphasize the importance of data quality considerations within the processing pipeline. Through a structured analysis of existing literature, we present a comprehensive overview of diverse big data analytical techniques, challenges, technologies, and applications, including statistical methods, machine learning algorithms, and distributed computing frameworks. Our key findings reveal the strengths and limitations of each approach, highlighting the need for hybrid solutions and domain-specific adaptations. Furthermore, we discuss the broader implications of big data processing on knowledge extraction, decision-making, and ethical considerations for responsible data analysis in various fields. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Big Data Big Data Analysis Data mining

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Hybrid Automated Malware Detection for Android User Data with Optimal Learning Approach 2

Hybrid Automated Malware Detection for Android User Data wit...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kumar, Mysugari Pradeep Chouhan, Arun Singh Department of Computer Science and Engineering School of Engineering Malla Reddy University Telangana Hyderabad India

ISBN: (纸本)9798350383867

The Android operating system is currently the most popular and widely used smartphone operating system. One reason for their appeal is that users can download and install free third-party programs that offer a number of benefits. Unfortunately, installing any third-party program has also led to an endless supply of malware programs that are always evolving and intended to harm users in different ways. This paper addresses and demonstrates a number of techniques to the test of Android malware detection. For a realtime detection system, data analytics are created. The installed applications may be searched by the detection system for ones that might be dangerous so they can be removed. Using machine learning models allows for this. The Automatic Android Malware Detection utilizing Optimal Ensemble Learning Approach for Cybersecurity (AAMDOELAC) technique is presented in this research proposal. Automated malware identification and categorization is the primary goal of the AAMD-OELAC approach. The AAMDOELAC approach does preliminary data preparation to accomplish this. The three machine learning models implemented in the ensemble learning phase of the AAMDOELAC methodology are the Regularized Random Vector Functional Link Neural Network (RRVFLN), Kernel Extreme Learning Machine (KELM), and Least Square Support Vector Machine (LSSVM). We utilize the Chi Square Model to extract data. The simulation findings show that the AAMD-OELAC technology outperforms other existing techniques. © 2024 IEEE.

关键词： Android malware

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A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model 2

A Fruit and Vegetable Detection Based on Pytorch Machine Lea...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Priyadharshini, U. Vijayan, R. School of Advanced Science Vellore Institute of Information Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Information Technology Vellore India

ISBN: (纸本)9798350328202

Today, Agricultures has become a significant element of people's life. Among them fruits and vegetables are a great thing for healthy lives. Classification of fruits and vegetable are difficult for normal person because of some same similarities like shape and color. Also, Fruits and Vegetables classification is an important process in industries applications as well as local market. Lots of similarities available between fruits like apples, pears and peaches, and that kinds of fruits are generally used in daily routine. As well as some vegetables are same in shape, so it will be difficult to differentiate in this type of situation for old persons. The previous research for solution of this type of problem for classification of fruits CNN is used but the problem in this research is when they use the self-made datasets then their accuracy decreases as compare to public datasets accuracy increases. To solve this type of problem, here produce a kind of algorithms in the paper to classify fruits and vegetable, using deep leaning technique such as VGG16 and DenseNet169 model have capability to extract the feature of images very deeply using various small filters so it provide a better result. On this premise, it was used to classify fruit kinds against a complex background and integrated with data improvement technology for future usage. It might be helpful for human for automatic differentiate the both categories. This project is implemented in the python programming language and the dataset used Fruits and Vegetables Image Recognition Dataset for the classification. Which will help to differentiate between Fruits and Vegetables with high accuracy rate. For the implementation of this project used Python programming and this implementation using the tool like Jupiter and Kaggle Notebook and used GPU for highspeed execution. © 2024 IEEE.

关键词： Fruits

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A Brief Comparative Study of Metaheuristic Approaches for Hyperparameter Optimization of Machine Learning Model 1

A Brief Comparative Study of Metaheuristic Approaches for Hy...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rathee, Manisha Kallam, Suresh Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India School of Computer and System Sciences Jawaharlal Nehru University India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350341737

Machine learning models have been successfully applied in numerous fields. Training a model is the most important aspect of machine learning for its successful application for the problem. To improve the training of a machine learning model thereby improve the performance, the selection of features and setting optimal parameters is crucial. Mainly two kinds of parameters are required to deal with, namely internal and external parameters. Internal parameters are model parameters and configurable such as weights of neural networks and their estimation can be done using data set. The hyperparameters such as learning rate, size of layers, number of layers, loss function etc, are external parameters and its values cannot be determined using the data set and it is not the part of the model. Its estimation can be done by the domain expert or using some trial-and-error techniques until it achieves some acceptable values. However, these techniques are highly time-consuming and cannot ensure the optimal values for these hyperparameters. In recent years different metaheuristic techniques have been applied to determine the optimal values of hyper parameters for machine learning models. In this paper we have conducted a brief comparative study of a few popular metaheuristic approaches applied for the hyperparameter optimization for various machine learning models. In this paper various evaluation measures have been considered for comparative analysis of metaheuristic approaches for hyperparameter optimization for deep learning model. © 2023 IEEE.

关键词： Ant colony optimization

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An FPGA Prototype for Parkinson’s Disease Detection using Machine Learning on Voice Signal

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IEEE Access 2025年 13卷 91113-91128页

作者： Khan, Mujeev Moiz, Abdul Khan, Gani Nawaz Wajid, Mohd Usman, Mohammed Ali, Jabir Z.H.C.E.T. Aligarh Muslim University Department of Electronics Engineering India Bennett University Department of Electronics and Communication Engineering India Bennett University School of Computer Science Engineering and Technology India

Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms—Chi-squared (χ2), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of 155× compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection. © 2013 IEEE.

关键词： Neurodegenerative diseases

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Arduino based fire detection alarm in rural areas

Arduino based fire detection alarm in rural areas

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Bhattarai, Omkar Dubey, Abhay Aditya Singh, Shashank Singh, Avjeet Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9781032669663

Fire is the major disaster worldwide, and even worst condition at the village. Hence, the fire detection system or alarm should accurately locate the fire in the shortest amount of time to reduce financial loss and environmental damage. Fire is a deadly element as well as a highly useful instrument in daily life, and it regularly causes catastrophes that are more expensive than money. Fire incidents typically result in the greatest amount of damage in the shortest amount of time since they happen quickly and leave no other salvageable parts behind. Early fire detection and prevention techniques have been developed to counteract this. In this paper, a tool for early fire detection and message sender response have both been created. The instrument is designed to find heat, gas, and flame possible fire indicators. The device is made up of an Arduino module, a GSMmodule for text messaging, sensors, a buzzer, and LEDs. It also has an acrylic circuit cover. Additionally, the gadget has been successful in text messaging the given cellphone number to notify the intended receiver of the events it has identified, such as excessive temperatures and gas leaks. It can show the faster response towards the fire and asks the alarm to respond quickly so that everyone gets informed. © 2024 The Author(s).

关键词： Fires

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Detection of Brain Stroke by using the Novel Adaboost Classifier Algorithm Classifier Algorithm Compared with Multi-layer Perceptron

Detection of Brain Stroke by using the Novel Adaboost Classi...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Manikanta, P. Kannan, K. Nattar Padmakala, S. Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Saveetha University Chennai India

ISBN: (纸本)9798350358926

Compare Novel Adaboost Classifier Algorithm and MultiLayer Perceptron to improve accuracy. Materials and Methods: Research groups include Novel Adaboost Classifier Algorithm and MultiLayer Perceptron. The 64.4 MB dataset includes 30 healthy and 30 damaged brain images. Kaggle provided CT images for brain stroke detection. Identifying the training dataset requires 500 records-80% for training and 20% for testing. A unique Adaboost method and a multilayer perceptron approach were compared using 10 sets to improve research accuracy. Ten times per iteration, twenty sets were compared. The G power test with α=0.05 and β=0.2 yields approximately 80%. A novel Adaboost classifier algorithm (88.84%) beats MultiLayer Perceptron (83.40%). The independent sample test value is significant at 0.01 (p © 2024 IEEE.

关键词： Machine learning

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Anomaly Detection in SCADA Industrial Control systems Using Bi-Directional Long Short-Term Memory 2nd

Anomaly Detection in SCADA Industrial Control Systems Using ...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Nakkeeran, M. Narayanan, V. Anantha Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819934805

In the modern era, industries are migrating to Industry 4.0 in the aspects of smarter and faster with efficiency to increase productivity to attain a profit. Thus, it becomes a part of Cyber-Physical systems (CPS) which is connected to the internet and pays a path for intrusion in the networks when dealing with the network traffic of ICS. In this paper, we evaluated the Industrial Control systems (ICS)/Supervisory Control and Data Acquisition (SCADA) Cyber Attack New Gas Pipeline Dataset from Mississippi State University with specialized two-time-series Recurrent Neural Network architectures of Deep Learning for anomaly detection which will act as an Intrusion Detection system (IDS). Here, we used two-time-series models: Bi-LSTM (Bi-directional long short-term memory) and Bi-GRU (Bi-directional Gated recurrent units) were used along with the LOCF (Last Observation Carried Forward) pre-processing technique to fill out the gaps and SMOTE (Synthetic Minority Oversampling Technique) for balancing the dataset. The main contribution of this paper involves identifying the attack type and its location related to specific attack classification apart from binary and category attacks classification which helps in preventing processing downtime from the zero-day exploits. Bi-LSTM outperforms better in all three types of attacks such as Binary (97.78%), Category (95.56%), and Specific (95.40%) in terms of accuracy metrics compared to Bi-GRU and other Deep learning methods. However, computation time is a trade-off between Bi-LSTM and Bi-GRU, resulting in more or less similar results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

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3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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