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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Prashanth, B.S. Manoj Kumar, M.V. Puneetha, B.H. Lohith, R. Darshan Gowda, V. Chandan, V. Sneha, H.R. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (纸本)9798350372892

In recent years, the application of artificial intelligence has revolutionized the field of lip reading by enabling the development of sophisticated models capable of accurately interpreting lip movements from video data. This work presents a novel deep learning approach to lip reading, focused on decoding spoken text from video sequences of lip movements. Traditional lip reading methods involve separate stages for visual feature design and prediction. The proposed system utilizes an endto-end deep learning model to directly map video frames to text transcriptions, leveraging 3D convolutional neural networks and bidirectional Long Short-Term Memory. By analyzing visual cues from lipmotions, the system can interpret speech, improving accessibility for individuals with hearing impairments and enabling communication in noisy environments. Compared to existing lip reading techniques, the deep learning model achieves superior performance on benchmark datasets, demonstrating its effectiveness in this challenging task. The model has achieved a character error rate of 1.54% and a word error rate of 7.96% on the best model. © 2024 IEEE.

关键词： Convolutional neural networks

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Empirical Evaluation of NoSQL and Relational Database Systems

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第8期14卷 2637-2650页

作者： Kanchan, Shivangi Kaur, Parmeet Apoorva, Pranjal Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Aim: To evaluate the performance of Relational and NoSQL databases in terms of exe-cution time and memory consumption during operations involving structured data. Objective: To outline the criteria that decision makers should consider while making a choice of the database most suited to an application. Methods: Extensive experiments were performed on MySQL, MongoDB, Cassandra, and Redis using the data for an IMDB movies schema prorated into 4 datasets of 1000, 10000, 25000 and 50000 records. The experiments involved typical database operations of insertion, deletion, update read of records with and without indexing as well as aggregation operations. Databases’ performance has been evaluated by measuring the time taken for operations and computing memory usage. Results: Redis provides the best performance for write, update and delete operations in terms of time elapsed and memory usage, whereas MongoDB gives the worst performance when the size of data increases, due to its locking mechanism. For the read operations, Redis provides better performance in terms of latency than Cassandra and MongoDB. MySQL shows the worst performance due to its relational architecture. On the other hand, Mon-goDB shows the best performance among all databases in terms of efficient memory usage. Indexing improves the performance of any database only for covered queries. Redis and MongoDB give good performance for range based queries and for fetching complete data in terms of elapsed time whereas MySQL gives the worst performance. MySQL provides better performance for aggregate functions. NoSQL is not suitable for complex queries and aggregate functions. Conclusion: It has been found from the extensive empirical analysis that NoSQL outperforms SQL based systems in terms of basic read and write operations. However, SQL based systems are better if queries on the dataset mainly involve aggregation operations. © 2021 Bentham Science Publishers.

关键词： Relational database systems

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Drone-Based Intelligent System for Social Distancing Compliance Using YOLOv5 and YOLOv6 with Euclidean Distance Metric

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SN computer Science 2024年第8期5卷 972页

作者： Parkavi, A. Alex, Sini Anna Pushpalatha, M.N. Shukla, Prashant Kumar Pandey, Ankur Sharma, Sachin Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering (AI and ML) M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Manav Rachna International Institute of Research and Studies Faridabad India

The COVID-19 pandemic forced rigorous social distancing measures to be put in place to halt the virus’s spread;compliance was extremely difficult. In response, we have designed a highly advanced intelligent drone system that maintains and controls distance measures following social distance principles through advanced machine learning skills. This paper describes an intelligent monitoring and compliance system in public places using drone technology with a focus on social distance requirements. Boosting the efficiency of human detection in real-time video streams acquired by drones, our solution employs the You Only Look Once version 5 (YOLOv5) and YOLO version 6 (YOLOv6) object detection algorithms which are the most potent computer vision tools today. The distances between the recognized individuals are computed based on the Euclidean distance measurement to distinguish cases of nonadherence to social distance measures. Our technique integrates the recently developed deep learning models with aerial imagery and monitoring to make it easily scalable for population health surveillance. Thus, we prove impressively that the underlying inventive method works well in different environmental conditions and can potentially contribute to people’s safety in the event of a pandemic. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Drones Euclidean distance metric Social distancing YOLOv6

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Endogenous Security of FQn Networks: Adaptive System-Level Fault Self-Diagnosis

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IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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Sustainable Algorithms using Artificial Intelligence and Various Stages for Precision Agricultural Cultivation 2

Sustainable Algorithms using Artificial Intelligence and Var...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kalaiselvi, V.K.G. Gopalakrishnan, Jagadeesh Anand, Sahana Shri Hariharan, Shanmugasundaram Saravanan, Sivasankaran Annamalai, Hemalatha Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350325799

India, the largest exporter of farm products, faces low agricultural productivity, resulting in reduced income for farmers. To boost revenue, performance improvement is crucial. Irrigation plays a vital role in farming, necessitating careful management of soil moisture levels. However, traditional irrigation methods often lead to water wastage. Efficient utilization of land and resources is essential in addressing this issue. Pest attacks pose a significant threat to crop yield and quality, making pest identification critical. Farmers and agronomists now utilize sensors and IoT technology to remotely monitor crops and manage them in controlled environments. However, crop productivity is still affected by weather conditions and disease variations. Precise detection of plant deficiencies and accurate fertilizer deployment is crucial for efficient farming. Modern computer vision techniques can be leveraged for positive financial and environmental outcomes in these tasks. Good crop selection significantly impacts yield, especially in developing countries. Additionally, growing vegetables in greenhouses or hydroponics offers a promising solution to discontinuous and costly supply chains. This abstract highlights the importance of addressing productivity challenges and implementing innovative farming methods to enhance agricultural outcomes. © 2023 IEEE.

关键词： Crops

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Malware Detection across Clusters

Malware Detection across Clusters

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Manjushree, C.V. Nandakumar, A.N. Vemana Institute of Technology Dept. of Information Science & Engineering India City Engineering College Dept. of Computer Science & Engineering India

ISBN: (纸本)9781665462631

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures. © 2023 IEEE.

关键词： Deep neural networks

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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ICStega: Image Captioning-based Semantically Controllable Linguistic Steganography 48

ICStega: Image Captioning-based Semantically Controllable Li...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Xilong Wang, Yaofei Chen, Kejiang Ding, Jinyang Zhang, Weiming Yu, Nenghai University of Science and Technology of China School of Cyber Science and Technology China Hefei University of Technology School of Computer Science and Information Engineering China

ISBN: (纸本)9781728163277

Nowadays, social media has become the preferred communication platform for web users but brought security threats. Linguistic steganography hides secret data into text and sends it to the intended recipient to realize covert communication. Compared to edit-based linguistic steganography, generation-based approaches largely improve the payload capacity. However, existing methods can only generate stego text alone. Another common behavior in social media is sending semantically related image-text pairs. In this paper, we put forward a novel image captioning-based stegosystem, where the secret messages are embedded into the generated captions. Thus, the semantics of the stego text can be controlled and the secret data can be transmitted by sending semantically related image-text pairs. To balance the conflict between payload capacity and semantic preservation, we proposed a new sampling method called Two-Parameter Semantic Control Sampling to cutoff low-probability words. Experimental results have shown that our method can control diversity, payload capacity, security, and semantic accuracy at the same time. © 2023 IEEE.

关键词： behavioral security image captioning linguistic steganography

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Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

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computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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Deep Learning Framework for Early Detection of Heart Attack Risk and Cardiovascular Conditions using Retinal Images 1

Deep Learning Framework for Early Detection of Heart Attack ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sakthidevi, I. Srinivasan, A. Gayathri, C. Julie, E. Golden Krishnan, R. Santhana Darney, P. Ebby Adhiyamaan College of Engineering Department of Information Technology Hosur India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350359756

In today's world, cardiovascular diseases including heart attacks are the prime reason for mortality. Detecting heart attack risk at early stages and accurate assessment of such risks related to cardiovascular conditions play an essential role in active patient management and preventive interventions. In the current research work, a novel deep learning framework called "CardioSightFrame"is proposed for detecting heart attack risks and cardiovascular conditions at early stages using retinal images. The proposed algorithm integrates Graph Convolutional Networks (GCNs) and Vision Transformers (ViTs) to control both local structural information and global contextual understanding from the retinal scans. A comprehensive simulation analysis is conducted to evaluate the performance of CardioSightFrame and it is compared with the existing algorithms. Also, the predictive accuracy of the proposed algorithm is measured using appropriate simulation metrics. By treating retinal images as graph nodes and leveraging self-attention mechanisms, the proposed algorithm achieves accurate and efficient feature extraction which is vital for early detection of heart attack risk and cardiovascular conditions. To evaluate CardioSightFrame's performance, wide-ranging simulation experiments are conducted using a varied dataset of retinal images. Through these comparisons, the predictive accuracy, sensitivity, specificity, and computational efficiency of CardioSightFrame are established. The simulation results indicate that CardioSightFrame outclasses existing algorithms in terms of accuracy and sensitivity for early detection of heart attack risk and cardiovascular conditions. The algorithm's ability to efficiently investigate retinal images with reduced computational overhead further enhances its applied pertinence. © 2023 IEEE.

关键词： Image enhancement

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