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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kiruba, Sweety Prasanna Sanu, Shubha G. Saranya, K. Rajani Kanth, Tatiraju V. Mahesh, S. Department of Computer Technology Bannari Amman Institute of Technology Erode India KLS Gogte Institute of Technology Affiliated Visvesvaraya Technological University Belagavi India Department of Electronics and Communication Engineering Sona College of Technology Salem India TVR Consulting Services Private Limited Hyderabad India Department of Computer Science and Engineering -AI Mar Baselios Christian College of Engineering and Technology Idukki Peermade India

ISBN: (纸本)9798331504960

The increasing sophistication of malware has posed significant challenges to cybersecurity, necessitating advanced approaches for malware detection and signature generation. This study presents a novel method for dynamic malware classification and signature generation using Multi-View Convolutional Neural Networks (MVCNNs). The proposed framework captures multiple perspectives of malware behavior, including system calls, memory access patterns, and network activities, to create a comprehensive representation of malicious activities. The MVCNN architecture leverages these diverse views to enhance classification accuracy, addressing the limitations of single-view analysis. The dynamic execution of malware in a sandboxed environment generates behavior logs, which are preprocessed and transformed into multidimensional feature maps for input into the MVCNN. The model then extracts high-level features across multiple views, enabling robust classification and the automated generation of unique malware signatures. Experimental results demonstrate the proposed method's effectiveness, achieving a classification accuracy of over 98% on benchmark datasets. Additionally, the generated signatures exhibit high precision and recall in identifying malware variants. The proposed approach not only improves malware detection rates but also provides scalability and adaptability for real-time cybersecurity applications. This research highlights the potential of multi-view deep learning frameworks to advance dynamic malware analysis and enhance cybersecurity resilience. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Classification Using Optimized Convolution Neural Network

Image Classification Using Optimized Convolution Neural Netw...

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engineering and Technology (PICET), Parul International Conference on

作者： Ramesh Chegoni V. SriLakshmi R. Sai Venkata Ramana Veeramreddy Rajasekhar Ravindra Changala Venkata Nagaraju Thatha Department of Information Technology CMR Technical Campus Hyderabad India Department of CSE(AI & ML) B V Raju Institute of Technology Narsapur India Computer Science & Engineering( Cyber Security) Department Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Malla Reddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Conventional artificial neural networks and machine learning algorithms encountered difficulties in fulfilling the processing needs for extracting features from large-scale images and training models. Furthermore, they showed poor efficiency and accuracy in classifying images. In order to lay a firm groundwork and offer aid for the categorization and recognition of large datasets, this study introduces a deep learning model for classification of images. The feature extraction technique of CNNs is refined by mitigating noise and adjusting model parameters, resulting in the development of an advanced CNN architecture for deep learning in image classification. The efficacy of the proposed model is assessed through tests that scrutinize the correlation between iteration numbers and accuracy of various network models are commonly employed in image classification. Notably, the research model surpasses its counterparts in terms of classification accuracy, indicating its superiority in the realm of image classification. The study contributes valuable insights by enhancing the understanding of CNNs and offering a more effective model for image categorization, supported by rigorous evaluations

关键词： Deep learning Training Accuracy Machine learning algorithms Image recognition Correlation Noise Feature extraction Image categorization Image classification

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Blockchain Technology to Improve Cybersecurity: Opportunities and Challenges 2

Blockchain Technology to Improve Cybersecurity: Opportunitie...

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2nd International Conference on Informatics, ICI 2023

作者： Jagadish, Sripelli Krishna, Somanchi Hari Sundaranarayana, D. Patel, Hemant N. Tiwari, Mohit Lakineni, Prasanna Kumar SR Universtity School of CS and AI Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management 501301 India Veltech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Sankalchand Patel University Sankalchand Patel College of Engineering Computer Engineering India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India GITAM University GITAM School of Technology Department of CSE Visakhapatnam India

ISBN: (纸本)9798350343830

Blockchain technology has gained widespread acceptance in numerous fields, most notably finance, thanks to the use of cryptocurrencies. The technology, on the other hand, can be used in cybersecurity. In order to enhance business processes and transform business operations, BCT is investigating cybersecurity issues and opportunities. This scoping review study was conducted with the intention of determining the scope of the current research and classifying various kinds of cybersecurity opportunities and challenges in BCT. A combination of phrases was used to search the Research Gate, IEEE, science Direct, Elsevier, and ABI/INFORM Collection (Pro Quest) databases. After a thorough screening, 55 research studies were determined to be relevant. According to our findings, the majority of publications discuss cybersecurity issues in BCT as a whole, whereas only a small number of papers discuss BCT's opportunities and challenges in cybersecurity in relation to specific industry sectors. In addition, previous research only briefly examined BCT's cybersecurity opportunities and challenges and solutions. Future study directions for researchers were suggested based on gap *** primary objective of this scoping review was to ascertain the current survey's size, scope, and gaps regarding BCT cybersecurity challenges. © 2023 IEEE.

关键词： Blockchain

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Faster Region-Convolutional Neural Network with ResNet50 for Video Stream Object Detection 3

Faster Region-Convolutional Neural Network with ResNet50 for...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Al-Jawahry, Hassan M. Hussein, A.H.A. Sunil, G. Alkhafaji, Mohammed Ayad Pareek, Piyush Kumar The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350317022

Detecting objects through video frames is a difficult process due to several issues, such as poor quality of frames, pose changes, occlusions, video defocusing, and motion blur. The closely arranged frames make it more complicated to detect the object in video streams. A common strategy employed to identify the problem is to correctly detect objects through video sequences. Considering these problems, the Faster Region-Convolutional Neural Network (FRCNN) is implemented to detect objects in video streams. At first, the Region proposal network (RPN) is used to generate the objects feature, and then the Convolutional Neural Network (CNN) classifies the RPN objects' features. Then the proposed architecture is combined with the ResNet50, which is the backbone of the Faster R-CNN pipeline, defining the FRCNN-ResNet50 object detector that constant detects objects in video streams. Then the model is evaluated in the DAVIS dataset and gets the F-measure 0.893, S-Measure 0.859, and Mean Absolute Error (MAE) 0.016 respectively. Compared to recent works like CSAttention-ConvLSTM, Deep-GCN, CFCN-MA, the proposed model achieves a higher detection rate and lower false detection. © 2023 IEEE.

关键词： Convolutional Neural Network Object Detection Occlusions Region Proposal Network Video Defocusing

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Student Data Analysis using Hadoop

Student Data Analysis using Hadoop

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International Conference on ent Computing and Control Systems (ICICCS)

作者： N. Shirisha G. Divyajyothi A. Prashanthi G. Sowmya Department Of Computer Science & Engineering MLR Institute Of Technology Hyderabad India Department Of Computer Science & Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India Department Of AI&ML MLR Institute Of Technology Hyderabad India

The amount of data produced at educational institutions today is growing quickly. It is challenging to analyze the student performance from the vast amount of data using conventional data processing methods. One can quickly analyze the vast amount of data by using big data. Previously, different databases are used for students. So that it takes more time to analyze the performance of a student. By using this application, it takes less time to analyze the data, as it is just like a centralized database where the entire profile of a student is found. In this study, data are analyzed using map-reduce techniques, which is a programming model suitable for processing large amounts of data, and a Hadoop cluster for ease of processing. This helps educational institutions analyze the students’ data better and focus on their weak spots.

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Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles 41

Towards Global Optimality for Practical Average Reward Reinf...

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41st International Conference on Machine Learning, ICml 2024

作者： Patel, Bhrij Suttle, Wesley A. Koppel, Alec Aggarwal, Vaneet Sadler, Brian M. Manocha, Dinesh Bedi, Amrit Singh Department of Computer Science University of Maryland College Park United States US Army Research Laboratory AdelphiMD United States JP Morgan AI Research New York United States School of Industrial Engineering Purdue University Indiana United States Department of Computer Sicence University of Texas Austin United States Department of Computer Science University of Central Florida Florida United States

In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significant challenge for the global convergence of policy gradient methods. This requirement is particularly problematic due to the difficulty and expense of estimating mixing time in environments with large state spaces, leading to the necessity of impractically long trajectories for effective gradient estimation in practical applications. To address this limitation, we consider the Multi-level Actor-Critic (MAC) framework, which incorporates a Multi-level Monte-Carlo (mlMC) gradient estimator. With our approach, we effectively alleviate the dependency on mixing time knowledge, a first for average-reward MDPs global convergence. Furthermore, our approach exhibits the tightest available dependence of O(√τmix) known from prior work. With a 2D grid world goal-reaching navigation experiment, we demonstrate that MAC outperforms the existing state-of-the-art policy gradient-based method for average reward settings. Copyright 2024 by the author(s)

关键词： Markov chains

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An Hybrid Authentication Mechanism for Cloud Server to Enhance the Computational Efficiency 4

An Hybrid Authentication Mechanism for Cloud Server to Enhan...

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Gandi, Ramakrishna Charan, K. Durga Bhanu Rajesh Naidu, Kamparapu Naidu, Kamparapu V.V. Satya Trinadh Raju, Yallapragada Ravi Rathinam College of Arts and Science-iNurture Department of Computer Science Coimbatore India Madanapalle Institute of Technology & Science Department of Computer Science - AI&DS Madanapalle India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665456357

The present computer era is based in large part on research, processing and safe storage in distributed computer networks of confidential data. In view of the development of various cryptographical algorithms, several research studies have addressed this issue. In addition to the stressful cryptographic studies, it is noted that the protection of data on the server side is vulnerable to handle, and there is need to build and effectively provable security framework that will boost the efficiency of Cloud Server data processing. This paper proposes a new protection scheme which authenticates evidence that sensitive data are stored safely when a cloud-central cloud storage architecture is deployed. This article introduces an innovative computer security mechanism that solves the issue of internal nodes by reducing the overhead computer with powerful security algorithms. The experimental study reveals that the proposed mechanism exceeds most current defense mechanisms. © 2022 IEEE.

关键词： Cloud storage

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DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 989-1001页

作者： Sen Yang Tao Shen Yuqi Fang Xiyue Wang Jun Zhang Wei Yang Junzhou Huang Xiao Han Tencent AI Lab Shenzhen 518057China Department of Electronic Engineering The Chinese University of Hong KongHong Kong Special Administrative Region 999077China College of Computer Science Sichuan UniversityChengdu 610065China

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology ***,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion ***,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental ***,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human *** validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy:99.596%),ranking the 2nd place among 866 participating *** promising result indicates the successful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery *** source code of Deep Noise is available at https://***/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

关键词： Fluorescent microscopy image Biological signal Classification Deep learning Genetic perturbation

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Collaborative Control of Aerial Robots for Inferring Human Intent from Gaze Following

Collaborative Control of Aerial Robots for Inferring Human I...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Chipade, Vishnu S. Gilbert, Alia Harari, Daniel Panagou, Dimitra University of Michigan Department of Aerospace Engineering Ann ArborMI United States University of Michigan Department of Robotics Ann ArborMI United States Weizmann Institute of Science Weizmann Ai Center Department of Computer Science and Applied Math Israel

ISBN: (纸本)9798350335446

In an ideal human-robot collaboration, autonomous robots work side-by-side with humans in a joint workspace, often performing complementary tasks to the humans. A robotic ability to infer human intention and goals directly from human behavior will facilitate the collaboration and maximize its efficiency. In this paper, we focus on inferring which object the human wants picked up next, based on what the human is looking at, by visually following the human gaze and head orientation. We develop a coordination protocol for a team of aerial robots to extract effective human head and gaze cues. The aerial robots are controlled to navigate around the human and collect data that improves the detection of the human's gaze and hence the intended object to be picked up. The effectiveness of the approach is shown using simulations in airSim, a photo-realistic simulator. © 2023 IEEE.

关键词： Antennas

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Document Image Text Extraction Using Emperor Penguin Optimization with Convolutional Neural Network 3

Document Image Text Extraction Using Emperor Penguin Optimiz...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Ramadan, Ghazi Mohamad Kumar, Bura Vijay Pareek, Piyush Kumar Adnan, Myasar Mundher Jabar, Muhammed Basim College of MLT Ahl Al Bayt University Karbala Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Text attained from natural scenes different information: accordingly, it is greatly utilized in different applications to understand the scenarios of image and also to retrieve the visual information. Human beings greatly benefit from the scene image's semantic content, which helps to understand their surroundings. The process of text identification and character recognition is more difficult in these natural images since the text presents a flexible appearance in an unrestricted environment. Therefore, implemented an Emperor Penguin Optimization (EPO) with Convolutional Neural Network (CNN)-based deep learning process is employed in this framework to effectually detect the text and to recognize the character from the scene images. To eliminate noise natural scene images, a grayscale conversion and image noise removal is typically applied during the preprocessing step. The DWT, LBP, and ResNet 50 techniques are used in feature techniques process, which are assists for the classification process. Following that, extracted features are selected by EPO technique. At last, with these selected features, the character recognition and text detection are achieved successfully by CNN techniques. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Emperor Penguin Optimization (EPO) Feature extraction Text, and OCR

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