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OBDTL: Under Water Object Classification Using Transfer Learning 4

OBDTL: Under Water Object Classification Using Transfer Lear...

引用

4th International Conference on computer, Communication, Control and information technology, C3IT 2024

作者： Roy, Sarthak Ghosh, Partha Goto, Takaaki Sen, Mainak Techno India University Computer Sc. & Engg. Dept. West Bengal Kolkata India Academy of Technology Adisaptagram Dept. of Computer Science & Engg. India Toyo University Dept. of Information Sciences and Arts Saitama Japan

ISBN: (纸本)9798350389128

Underwater object classification is a critical task in domains such as marine research, underwater exploration, maintenance of underwater structures and deep-sea oil industries etc. Accurate detection of underwater objects is a task of vital importance for autonomous underwater vehicles (AUVs). Tasks such as obstacle avoidance and autonomous navigation become easier when underwater obstacles can be recognized. A learningbased, robust object detection model capable of processing images at a high rate is beneficial for real-time object detection. This project focuses on developing a robust underwater object classification system utilizing machine learning techniques to classify objects present in underwater imagery. In this paper, we explore the efficacy of transfer learning using VGG16. VGG16 offers valuable pre-trained weights that encapsulate knowledge about fundamental image features. Here, we have trained a convolutional neural network and a VGG-16 based neural network classifier on three datasets and received 84.89% of accuracy with VGG-16 based classifier using transfer learning. © 2024 IEEE.

关键词： Convolutional neural networks

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A Case Study of Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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Steganalysis on Dual-Layer Security of Messages Using Steganography and Cryptography 15

Steganalysis on Dual-Layer Security of Messages Using Stegan...

引用

15th International Conference on information technology and Electrical Engineering, ICITEE 2023

作者： Shamsi, Zeba Saha, Anish Kumar Patgiri, Ripon Singh, Khoirom Motilal Singh, Laiphrakpam Dolendro National Institute of Technology Dept. of Computer Science and Engineering Assam Silchar788010 India Indian Institute of Information Technology Dept. of Computer Science and Engineering Manipur Senapati795002 India

ISBN: (纸本)9798350304466

Recently, Bansal et al. proposed a dual-layer message security technique using steganography and cryptography. An index table maps each text data to an index point and an elliptic curve point. The text data are encrypted using elliptic curve cryptography, and the corresponding index values are embedded in a cover image using the Least Significant Bit Inversion technique. The proposed paper breaks the security of the key exchange technique in the Bansal et al. method and the secret text is extracted. © 2023 IEEE.

关键词： Steganography

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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Ensuring Electronic Health Record (EHR) Privacy using Zero Knowledge Proofs (ZKP) and Secure Encryption Schemes on Blockchain 5

Ensuring Electronic Health Record (EHR) Privacy using Zero K...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Ranaweera, T.A.V.Y. Hewage, H.N.H. Hapuhinna, H.K.D.W.M.C.B. Preethilal, K.L.K.T. Senarathne, Amila Ruggahakotuwa, Laneesha Sri Lanka Institute of Information Technology Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud. © 2023 IEEE.

关键词： Blockchain

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Malware Detection across Clusters

Malware Detection across Clusters

引用

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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A Deep Learning-based MobileNet Model for Effectively Classifying the White Blood Cancerous Microscopic Images 14

A Deep Learning-based MobileNet Model for Effectively Classi...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Srivastava, Neha Singh, Sunil Kumar Dept. of Computer Science and Information Technology Mahatma Gandhi Central University Bihar Motihari India

Traditional machine and deep learning-based techniques effectively utilise advanced medical image analysis (MIA) to improve prediction accuracy and enable optimal planning and diagnosis. This work aims to create an automated classification system using various machine, deep, and ensemble learning-based models. The dataset used in the work contains the B-lineage acute lymphoid leukaemia and Multiple myeloma microscopic images. The models’ performance are evaluated using the Accuracy, precision, recall, specificity, and F1-Score as performance metrics. The findings of the work states that MobileNet is one of the best-performing models and its accuracy is approx. 98.04%. A comparative study is also done to prove the efficacy of the model. © Grenze Scientific Society, 2023.

关键词： Learning systems

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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KinderEye: Kindergarten Child Performance Monitoring System 9

KinderEye: Kindergarten Child Performance Monitoring System

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9th International Conference on computer and Communication Engineering, ICCCE 2023

作者： Rathnayaka, B. Rabel, W.T.S. Namasingha, C.N.W. Hettiarachchi, H.D. Hansika, Mahaadikara M.D.J.T. Wellalage, Sasini Nuwanthika Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350325218

Kindergarten is the most important beginning of a child's formal education. Since the brain is rapidly developing in early childhood, a lack of brain development at kindergarten can create a weak foundation for learning, health, and behavioral problems throughout life. Digital learning and monitoring applications for young children are widely available but often lack opportunities for physical engagement, which can negatively impact executive function development. In this paper, we introduce a system for continuously monitoring mental development in terms of working memory, decision-making ability, language skills, and likelihood of having ADHD. The system consists of three physically playable games that collect performance data from digital sensors and encourage children to be involved in physical activities while increasing enthusiasm for evaluations. Continuous performance measurement allows teachers and parents to know the development of children and guide them or direct them to have suitable medical treatments. Memory development evaluation is done from the button-pressing game which allows giving inputs according to a pattern previously indicated. For decision-making ability measurement, we are using a remote controllable robot and maze in which children navigate the robot toward the winning point of the maze. Language development is measured by giving several activities through an attractive device that measures listening, speaking, reading, and writing. Furthermore, checking ADHD is done by lively tracking child behavior from a camera while playing a game and invalid game inputs. © 2023 IEEE.

关键词： Decision making

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Secure and Efficient Key Management for Deduplicated Cloud Storage Systems 16

Secure and Efficient Key Management for Deduplicated Cloud S...

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16th International Conference on Security of information and Networks, SIN 2023

作者： Dave, Jay Kanodia, Anshul Srivastava, Raj Singh, Kushagra Hegde, Prithvi Desai, Hitaishi Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad Pilani India

ISBN: (纸本)9798350343212

Deduplication is a ubiquitous compression technique for cloud storage servers. It reduces storage and bandwidth requirements by avoiding duplicate copies of data. However, developing an encryption scheme for deduplication is a critical challenge because traditional encryption techniques are not compatible with deduplication. The existing state-of-the-art encryption schemes are at risk of brute-force attacks as they use deterministic techniques for key generation. Moreover, the user in the recent schemes suffers from storage overhead of deduplication metadata and encryption keys. In this paper, we introduce a secure and efficient key management scheme for deduplication. In our scheme, the user divides the file into blocks and encrypts each block using a random key. As a result, our scheme protects against brute-force attacks. In addition, the user securely shares the encryption keys on public storage servers with the aid of a private server. As a result, the proposed approach causes lower storage overhead. We implement our scheme in a real cloud scenario and evaluate the performance of our approach in terms of storage and computation overhead. © 2023 IEEE.

关键词： Cloud storage

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