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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vullam, Nagagopiraju Jagadeesh, S.V.V.D. Pavani, K. Siva Janakidevi, Maddala Lakshmanarao, A. Chandreswari, Y.S.N. Chalapathi Institute of Engineering and Technology Department of Cse A.P Guntur India LakiReddy Bali Reddy College of Engineering Department of Ai&ds Mylavaram India Aditya University Department of Ai&ml Surampalem India Srkr Engineering College Department of Cse A.P Bhimavaram India Aditya University Department of It Surampalem India Bonam Venkata Chalamayya institute of technology and science Department of Mca Batlapalem India

ISBN: (纸本)9798331529635

Brain stroke is a life-threatening condition that needs to be detected early and correct precision to improve patient wellness. This paper presents a method for predicting stroke using ml and ANN using a publicly available data from Kaggle. The dataset has class imbalance. This can be solved using SMOTE and ADASYN to guarantee a balanced class distribution. After preprocessing, six traditional ml models including Logistic Regression, DTC, Random Forest, KNN, SVM and Gradient Boosting were used to predict the risk of stroke. To further increase the accuracy of the forecast. Later, ensemble models also applied to further enhance performance. Finally, ANN was applied to capture complex patterns in the data. The results shown that the ensemble model significantly improves the prediction performance compared to the individual ml models. Later a hybrid ml and DL ANN is proposed. The proposed hybrid model combining ml best model and ANN technique improved the accuracy in predicting stroke and provide a more reliable system for early detection of stroke. It is observed that the hybrid method will outperform both individual ml models and ensemble models. © 2024 IEEE.

关键词： Adversarial machine learning

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview 1

Wireless Sensor Networks and Green Networking for 6G communi...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bordoloi, Dibyahash Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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Internet of Intelligent Things (IoT) - An Overview 1

Internet of Intelligent Things (IoT) - An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate ai and ml within the IoT's interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide. © 2023 IEEE.

关键词： Internet of things

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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Deep Learning Technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrated CNN and Recurrent Neural Network Model for Phishing Website Detection 3

Integrated CNN and Recurrent Neural Network Model for Phishi...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Sree, K.Kavya Ramya Anusha, Chamarty Lagadapati, Yuvana Babu, Vunnava Dinesh Vullam, Nagagopiraju Lakshmanarao, A. Aditya University Department of AI&ML Surampalem India Vijaya Engineering College Department of Data Science Khammam India RV Institute of Technology Department of CSE Guntur India Chalapathi Institute of Engineering and Technology Department of CSE Andhra Pradesh Guntur India Aditya University Department of IT Surampalem India

ISBN: (纸本)9798350354171

The rapid proliferation of malicious URLs poses significant threats to cybersecurity, necessitating the development of robust detection mechanisms. This research explores a comprehensive approach for malicious URL detection by leveraging both ml and DL models. Initially, baseline models including RNN, LSTM, Gated Recurrent Unit (GRU), as well as ml classifiers such as XGBoost, Random Forest, and Gradient Boosting were implemented to establish performance benchmarks. Subsequently, a more sophisticated model combining Convolutional Neural Networks (CNN) with LSTM layers was developed to capture both local patterns and long-term dependencies in URL sequences. The dataset of text URLs from Kaggle was preprocessed through tokenization and sequence padding to facilitate model training. Evaluation of the models was performed using a variety of metrics including accuracy, precision, recall, F1-score, and ROC-AUC, providing a comprehensive assessment of their efficacy. The results indicated that the CNN+LSTM model significantly outperformed the baseline models, demonstrating its potential for effective malicious URL detection. This work contributes to the field by presenting a detailed methodology for developing and optimizing deep learning models tailored to cybersecurity applications. © 2024 IEEE.

关键词： Phishing

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Secure and Efficient Group Decision-Making with Blockchain-Based Consensus and Trust Management

Secure and Efficient Group Decision-Making with Blockchain-B...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Hassani, Hossein Razavi-Far, Roozbeh Saif, Mehrdad Herrera-Viedma, Enrique University of Windsor Department of Electrical and Computer Engineering Windsor Canada Canadian Institute for Cybersecurity University of New Brunswick Faculty of Computer Science Fredericton Canada University of Granada Department of Computer Science and Ai Granada Spain

ISBN: (纸本)9798350337020

Trust-building is of paramount importance for managing and improving consensus in group decision-making (GDM). This mechanism usually involves a trust propagation process for estimating the level of trust among decision-makers (DMs). However, this process is computationally expensive and hinders the speed of consensus reaching. To address this issue, this work proposes a novel trust-building mechanism that does not rely on the trust propagation process to quantify DMs' level of trust. Instead, it makes use of Blockchain technology to facilitate communication between the moderator and the group of DMs. This novel trust-building mechanism does not rely on trust propagation, which makes it computationally efficient for building trust among DMs while also providing a secure and efficient communication protocol to accelerate the consensus-reaching process. The proposed GDM model is illustrated through an example, and the sensitivity of the model to various assumptions is analyzed, demonstrating the practical applicability of this approach. © 2023 IEEE.

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Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection

Hybrid Optimization Based Long Short-Term Memory for Android...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Lande, Jayapal Maheswari, M. Kamali, S.M. Dineshkumar, R. Priyanka, M.S. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Chennai India Annapoorana Engineering College Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Department of Ece Sriperumbudur Thandalam India Nitte Meenakshi Institute of Technology Department of Ai and Ml Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Malware detection is process of detecting the suspicious files and activities, that can classify as benign and malicious depends on different characters like capability of infection, structure of malware and speed rate. In this paper, proposed a Particle Swarm Optimization and Ant Colony Optimization (HPSALO) based Long Short-Term Memory (LSTM) model for malware detection and classification. The combination of these two algorithms that leads to the balance between exploration and exploitation. The dataset utilized for the research is DREBIN dataset and process of data cleaning is performed by NumPy library. The feature selection is performed by combination of hybrid PSALO algorithms and selected features are given to Long Short-Term Memory (LSTM) classifier that classifies as benign and malware files. The performance of proposed method is estimated by performance metrics of accuracy, precision, recall and f1-score. The proposed method attained accuracy of 99.38%, precision of 99.30%, recall of 99.08% and f1-score of 99.42% which is comparatively higher than other existing methods like Whale Optimization Algorithm-LSTM (WOA-LSTM), Cat Swarm Optimization-LSTM (CSO-LSTM), Particle Swarm Optimization-LSTM and Ant Lion Optimization-LSTM. © 2023 IEEE.

关键词： Ant Lion Optimization Long Short-Term Memory Malware Detection NumPy and Particle Swarm Optimization

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Crop Disease Detection with Autonomous Robotic Systems

Crop Disease Detection with Autonomous Robotic Systems

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Women in Computing (InCoWoCo), International Conference for

作者： M Rithani KJ Shabarirajan B Shivanesh RS SyamDev Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI & ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Crop disease detection is an important maintenance aspect for the upkeep of agricultural productivity. This paper presents a novel autonomous robot system designed to navigate and monitor crop health. The system was developed within a ROS framework using a camera to capture real-time video feeds of crops and a YOLO v8 model for detecting the presence of three specific plant diseases. Such a configuration resembles a farm in the presence of crops and trees and hence can be termed as a dirty farm. Automated navigation will cover all the area. The detection model processes each frame to detect plant disease. Such an approach has the potential to make improvements in early detection of diseases, thus leading to proper management and informed decisions about precision agriculture.

关键词： YOLO Productivity Precision agriculture Plant diseases Navigation Robot vision systems Crops Streaming media Real-time systems Monitoring

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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

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International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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