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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Automatic text inpainting and quality elevation in video sequences

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Multimedia Tools and Applications 2024年 1-34页

作者： Palivela, Lakshmi Harika Dharmalingam, Vivekanandan Gayathri, D. Bala School of Computer Science and Engineering Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Chennai600127 India Department of Information Technology Madras Institute of Technology Anna University Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute Of Science and Technology Chennai India

Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu

关键词： Convolutional neural networks

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A systematic review of multilabel chest X-ray classification using deep learning

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Multimedia Tools and Applications 2024年 1-35页

作者： Hasanah, Uswatun Leu, Jenq-Shiou Avian, Cries Azmi, Ihsanul Prakosa, Setya Widyawan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Chest X-ray scans are one of the most often used diagnostic tools for identifying chest diseases. However, identifying diseases in X-ray images needs experienced technicians and is frequently noted as a time-consuming process with varying levels of interpretation. In particular circumstances, disease identification through images is a challenge for human observers. Recent advances in deep learning have opened up new possibilities for using this technique to diagnose diseases. However, further implementation requires prior knowledge of strategy and appropriate architecture design. Revealing this information, will enable faster implementation and encounter potential issues produced by specific designs, especially in multilabel classification, which is challenging compared to single-label tasks. This systematic review of all the approaches published in the literature will assist researchers in developing improved methods of whole chest disease detection. The study focuses on the deep learning methods, publically accessible datasets, hyperparameters, and performance metrics employed by various researchers in classifying multilabel chest X-ray images. The findings of this study provide a complete overview of the current state of the art, highlighting significant practical aspects of the approaches studied. Distinctive results highlighting the potential enhancements and beneficial uses of deep learning in multilabel chest disease identification are presented. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Improper Implementation of Transport Layer Security: Impact on Android Applications and Man-in-the-Middle Attack 2

Improper Implementation of Transport Layer Security: Impact ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Moon, Iffath Tanjim Mimi, Afsana Rahman Mridha, Md. Musfiqur Pundra University of Science & Technology Department of Computer Science & Engineering Bogura Bangladesh Varendra University Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

In this paper, we have analyzed the impact of improper implementation of Transport Layer Security (TLS) in Android applications. Communication security in Android applications is largely dependent on Transport Layer Security. For instance, data encryption, data integrity, authentication, trust-building, network attack prevention, and API and web service security are all provided by it. TLS preserves confidence and aids in preventing the unwanted exposure of sensitive data. However, improper implementation of TLS can pose a serious threat to the cybersecurity of an application. Therefore, software developers should ensure the security of applications, foster client confidence, and adhere to legal standards by skillfully implementing TLS. In this paper, we have chosen 25 miscellaneous Android applications and performed static analysis on them. Furthermore, we have identified various improper implementations of TLS in these applications. The riskiest TLS implementations in these applications include insufficient verification of the TLS certificate chain, invalid TLS certificates, insufficient checking of the TLS certificate validity, and insecure WebViews usage. For these vulnerable implementations of TLS, some applications are prone to a man-in-the-middle (MitM) attack. At last, we have provided the best practices of TLS implementations for Android applications. In addition, we have provided remediation for the vulnerabilities so that applications can be protected against MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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Path Authentication for Multiple Switches within First Communication Packets 6

Path Authentication for Multiple Switches within First Commu...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Anna, Koziol Agata Shen, Shan-Hsiang Shen, Chung-An Huang, Chin-Ya National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taiwan National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taiwan

ISBN: (纸本)9798350378511

This paper introduces a new approach to switch authentication within a network environment, addressing the challenges associated with multiple switch configurations. The proposed continuous authentication process is seamlessly integrated into the TCP handshake, leveraging public-private key encryption and challenge-based authentication methodologies. This novel solution aims to improve performance of traditional one-to-one authentication methods, both in terms of efficiency and security. The research demonstrates the efficiency gains achieved by the proposed authentication process through a comparative analysis with one-to-one solution based on the same authentication mechanism. The results reveal a significant reduction in authentication time, thanks to link latency reduction. The incorporation of public-private key encryption enhances the security, ensuring protection against unauthorized access and switch spoofing attempts. © 2024 IEEE.

关键词： Authentication Protocol

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Decoupling and Interaction: task coordination in single-stage object detection

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Multimedia Tools and Applications 2025年第10期84卷 8149-8178页

作者： Ma, Jia-Wei Tian, Shu Man, Haixia Chen, Song-Lu Qin, Jingyan Yin, Xu-Cheng Department of Computer Science and Technology University of Science and Technology Beijing No. 30 Xueyuan Road Haidian District Beijing100083 China School of Foreign Studies University of Science and Technology Beijing No. 30 Xueyuan Road Haidian District Beijing100083 China

In the field of computer vision, general single-stage object detection methods employ two individual subnets within detection head, serving classification and localization purposes respectively. However, the lack of explicit modeling for distinctions and associations poses challenges for aligning the spatial feature perception of these two tasks, consequently leading to sub-optimal detection performance. Although some methods utilize classification to evaluate localization, it is a compromise rather than multi-task optimization. In this paper, we propose a Task-coordinated Single-stage Object Detector (TSOD) to enhance the coordination of multiple tasks. Firstly, we introduce a Task-decoupled Feature Alignment Mechanism (TFAM), which adaptively provides compatible features for different tasks by decoupling spatial information. For classification and localization, the network adaptively samples from category-sensitive regions and boundary-separable regions, respectively. Secondly, we propose a Task-interactive Enhancement Mechanism (TEM), which explicitly combines different task-sensitive features for joint classification score prediction and selects samples with high task consistency for training. Through this interaction mechanism, consistency between tasks is bolstered. We conduct extensive experiments on the COCO, Cityscapes, CrowdHuman and WiderFace datasets to evaluate the performance of TSOD. The results demonstrate that our model outperforms several state-of-the-art detectors, achieving a 2.0 AP improvement over the baseline on COCO minival and a remarkable 50.4 AP at single-model single-scale testing on COCO test-dev. Additionally, our model, equipped with ResNet-50, performs significantly better than other representative detectors on the Cityscapes, CrowdHuman, and WiderFace datasets, showcasing its robustness and generalizability. Our study contributes a new perspective to the design of single-stage object detectors by emphasizing the importance of decoupl

关键词： Object detection

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Dual-Cloud Multi-Secret Sharing Architecture for Privacy Preserving Persistent Computation

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IEEE Transactions on Information Forensics and Security 2024年 19卷 7523-7535页

作者： Chen, Yu-Chi Yang, Jhe-Kai Yen, Hsin-Chan Lin, Pei-Wen National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan Yuan Ze University Department of Computer Science and Engineering Taoyuan32003 Taiwan Genie Networks Taipei114064 Taiwan

With the prevalence of artificial intelligence, people collect data through numerous sensors and use machine learning to create models for intelligent services. However, data privacy and massive data issues are raised with the proliferation of devices. Although secret sharing can be the solution to providing privacy and performing efficient computations (than homomorphic encryption-based) in the privacy domain, sharing large amounts of data may impose a considerable storage burden on resource-restricted devices. Therefore, we aim to reduce the shares that participants need to hold and construct secure computation protocols that form privacy-preserving data utilization with multi-secret sharing. In this paper, we study the mechanism of persistent computation with multi-secret sharing for privacy protection, which avoids the requirements for participants to store extra information during secure computation. We present the mask techniques to convert secrets into protected data and upload them separately to non-colluding dual-cloud servers through multi-secret sharing. Then, we can locally evaluate the operation result shares by revealing a part of the protected data, where the true secret consistently remains secure as the other part stays in a shared state. Eventually, we outsource a dataset to the cloud, build a privacy-preserving multi-party kNN classification based on our scheme, and provide some experiments to demonstrate the feasibility and usability of storage size. © 2005-2012 IEEE.

关键词： computer architecture

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Building an effective KPI monitoring system for an OTT Platform 4

Building an effective KPI monitoring system for an OTT Platf...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jena, Biswaranjan Mishra, Debahuti Mishra, Smitaprava University Department of Computer Science and Engineering Bhubaneswar India University Department of Computer Science and Information Technology Bhubaneswar India

ISBN: (纸本)9798350349856

Success of any Over the Top (OTT) platform depends on how the platform is providing best user experience along with content to its customers. Being in media and entertainment space customers need to access content from various devices like mobile, tab, desktop, tv including set top boxes. To measure the effectiveness of the systems its inevitable to define standard Key Performance Indicators (KPIs) and continuously monitor the application along with standard monitoring capabilities at infrastructure level to make the application available with required throughput. This paper describes in detail various application KPIs respective to different channels and systems along with tools used to measure the same. © 2024 IEEE.

关键词： Benchmarking

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Improved Set Algebra-Based Heuristic Technique for Training Multiplicative Functional Link Artificial Neural Networks for Financial Time Series Forecasting

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SN computer science 2024年第5期5卷 1-34页

作者： Behera, Sudersan Kumar, AVS Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering & amp Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Exchange rate forecasting Improved Set Algebra-Based Heuristic Algorithm Multiplicative functional link artificial neural network Stock market forecasting

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