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检索条件"丛书名=Lecture Notes in Networks and Systems"

共 9905 条记录，以下是2351-2360 订阅

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Improving Password Generation Algorithm with Parallellism: Comparative Performance Study 13th

Improving Password Generation Algorithm with Parallellism: C...

引用

13th Computer Science Online Conference

作者： Albarakati, Sumayyah Almajnoni, Atheer Alqthami, Deema Mujahed, Lama Yassin, Renad Aljabri, Malak Alqarawi, Seba Al-Eidarous, Walla Umm Al Qura Univ Coll Comp Comp Sci & Artificial Intelligence Dept Mecca Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Engn POB 1982 Dammam 31441 Saudi Arabia Umm Al Qura Univ Coll Comp Comp & Network Engn Dept Mecca 21955 Saudi Arabia

ISBN: (纸本)9783031705946;9783031705953

Passwords are crucial for online security, serving as the primary defense for critical accounts and sensitive data. However, evolving cyber threats challenge password resilience, necessitating robust solutions. This paper investigates PassGAN, a deep-learning approach that surpasses traditional methods by leveraging machine learning algorithms for password generation. The study optimizes PassGAN's performance through parallel computing, addressing time-intensive complexity. Experiments show a significant improvement in PassGAN's efficiency with multiprocessing. Using four processing elements, it generates 500,000 passwords in 1024 s, demonstrating a 3.1x speedup. This parallelization empowers PassGAN to efficiently produce diverse passwords, enhancing its overall effectiveness in password security. This research underscores PassGAN's potential, augmented by parallel computing, as a formidable tool against cyber threats, emphasizing its ability to enhance secure password generation.

关键词： Parallel processing Parallel computing Machine learning Deep learning Password generation

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Linking Mental Health Incidents with External Variables: A Machine Learning Study 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Rosa-Remedios, Carlos Gonzalez-Quintana, Noemi Molina-Gil, Jezabel Caballero-Gil, Pino Univ La Laguna San Cristobal la Laguna Spain Servicio Urgencias Canario Las Palmas Gran Canaria Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

This study explores the correlation between mental health incidents and external socioeconomic variables. Using machine learning techniques, the data to identify patterns and predict mental health risks is analysed. The research includes a detailed analysis that provides a comprehensive overview of how these external factors influence mental health. The results obtained are promising and demonstrate the potential of machine learning to improve public health strategies. As ongoing work, quantum machine learning techniques are being applied to further refine the proposed predictive models and improve accuracy.

关键词： Mental Health Risk Mapping Machine Learning Predictive Analysis Socio-economic Factors

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Breast Cancer Stage Determination Using Deep Learning 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Aniq, Elmehdi Chakraoui, Mohamed Mouhni, Naoual Aboulfalah, Abderrahim Rais, Hanane Sultan Moulay Slimane Univ Polydisciplinary Fac Khouribga LS3ME Beni Mellal Morocco EMSI Marrakech LAMIGEP Marrakech Morocco UMI Meknes Fac Sci & Tech Dept Informat GL ISI Team Errachidia Morocco Cadi Ayyad Univ Fac Med & Pharm Dept Obstet Gynecol Marrakech Morocco Cadi Ayyad Univ Fac Med & Pharm Dept Pathol Anat Marrakech Morocco

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Ki-67 is a non-histone nuclear protein located in the nuclear cortex and is one of the essential biomarkers used to provide the proliferative status of cancer cells. Because of the variability in color, morphology and intensity of the cell nuclei Ki-67 is sensitive to chemotherapy and radiation therapy. The proliferation index is usually calculated visually by professional pathologists who assess the total percentage of positive (labeled) cells. This semi-quantitative counting can be the source of some inter- and intra-observer variability and is time consuming. These factors open up a new field of scientific and technological research and development. Artificial intelligence is attracting attention to solve these problems. Our solution is based on deep learning to calculate the percentage of cells labeled by ki-67 protein. The tumor area with x40 magnification is given by the pathologist to use to segment different types of positive, negative or TIL (tumor infiltrating lymphocytes) cells. The calculation of the percentage comes after the counting of the cells using classical image processing techniques. To give the model our satisfaction, we make a comparison with other datasets of the test and we compare it with the diagnosis of pathologists.

关键词： Ki-67 Proliferation index Deep learning Medical Artificial intelligence Medical imaging Digitization of Ki-67 breast cancer

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Task-Driven Semantic-Aware Image Compression 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Zhou, Junwei Xiao, Haoyun Li, Dexin Wang, Liangliang Liang, Peidong Zhou, Jian Yang, Yanchao Wuhan Univ Technol Luoshi Rd 122 Wuhan 43007 Peoples R China Fujian Key Lab Intelligent Operat & Maintenance R Quanzhou Software Pk 9 Quanzhou 362008 Peoples R China Fujian Quanzhou Inst Adv Mfg Technol Quanzhou Software Pk 5 Quanzhou 362008 Peoples R China

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In the context of IoT/IoE, efficient image compression is crucial for reducing bandwidth consumption and enabling real-time data processing. Predominantly, existing compression techniques are designed with an emphasis on human visual perception, frequently overlooking the specific demands of tasks involving image analysis. This paper introduces a novel Task-Driven Semantic-Aware Image Compression (TDSIC) approach with a feature enhancement network engineered to retrieve the latent semantic information typically lost in entropy-decoded results. TDSIC also leverages importance maps as a form of prior knowledge, which assists in guiding the bit allocation strategy of the compression technique. By refining the perceptual loss function, the network's emphasis is realigned to prioritize the retention of semantically significant information. Additionally, the training regimen incorporates insights from discriminative networks to harness feedback on human visual discernment, which is instrumental in meticulously calibrating the perceptual understanding of the image compression model. This calibration ensures that the model adeptly captures and correlates pertinent information. TDSIC achieves up to a 12% improvement in Top-1 image classification accuracy on the ILSVRC2012 dataset and up to a 0.3% enhancement in object detection on the COCO dataset over baseline methods. The Kodak dataset experiments underscore that images compressed with our method not only maintain visual appeal to human viewers but also enhance the performance of specific tasks. Supplementary investigations, including ablation studies on the feature enhancement module (FEM), indicate that the FEM is adept at recovering information that was previously lost during the encoding and quantization phases. The implementation code is made publicly accessible at https://***/lie209/TDSIC.

关键词： Image Compression Semantic-Aware Machine Vision

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RHLab Interoperable Software-Defined Radio (SDR) Remote Laboratory 21st

RHLab Interoperable Software-Defined Radio (SDR) Remote Labo...

引用

21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Inonan, Marcos Zhang, Zhiyun Amarante, Pedro Orduna, Pablo Hussein, Rania Arabshahi, Payman Univ Washington Seattle WA 98195 USA LabsLand San Francisco CA 94114 USA

ISBN: (纸本)9783031619045;9783031619052

Educational remote laboratories, dedicated to radio frequency (RF) communications, represent an innovative effort aimed at bridging the gap between theory and practice. This advancement is even more empowered by the advent of Software-Defined Radio (SDR) technology, which allows students to create a wide array of prototypes for RF communication systems. The SDR community provides highly adaptable hardware that can be easily customized with just a few lines of code. Additionally, the availability of SDR hardware is extensive, offering various features tailored to users' specific needs. One of the key advantages of the flexibility inherent in SDR technology is the shared libraries among its devices. This feature fosters interoperability which is the ability of different SDR devices or components to work together effectively. In this context, the Remote Hub Lab (RHL/RHLab) has been working to incorporate this functionality into the RHL-RELIA remote lab project. RHL-RELIA stands as a remote wireless communication lab, with its initial iteration based on the ADALM-PLUTO SDR. However, as it evolves, RHL-RELIA is incorporating other SDR devices, such as the Red Pitaya, which introduces unique features not found in the ADALM-PLUTO. This degree of diversification empowers students with a broad spectrum of choices, allowing them to select the SDR device that aligns most closely with their individual interests and educational requirements.

关键词： GNU Radio Interoperability Software-Defined Radio Remote Laboratory Embedded systems

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A Model of Heterogeneous Display Component for Cyber-Physical systems Simulation

A Model of Heterogeneous Display Component for Cyber-Physica...

引用

Computing Conference

作者： Feredj, Mohamed Laichi, Boualem Univ Sci & Technol Houari Boumediene Fac Comp Sci 32 Al Alia Algiers Algeria

ISBN: (纸本)9783031622830;9783031622816;9783031622809

Cyber-Physical systems (CPSs) modeling consists to release an executable system model. The modeling process is composed of steps where each step is the refinement of the previous step with more details and the last step is the validation step. The validation of system can be done formally or by simulation. Since CPSs are heterogeneous and combine physics and computations, the formal validation is often difficult to apply and the validation by simulation is the only method to verify the correctness of the modeling process. The simulation of CPSs generates heterogeneous output data. So, the display of heterogeneous output data involves the use specific display components (SDCs) where each SDC can display one and only one type of output data. Indeed, the presence of important number of SDCs makes the design task not easy, because the knowledge of SDCs is indispensable for the simulation, and it prevents the reusability. As solution, we propose in this paper a model of heterogeneous display component (HDC) that allows the designer to use only one component to display different types of output data.

关键词： Cyber physical systems Heterogeneous models Heterogeneous simulation Model of computation Actor methodology Software engineering

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Deep Learning for Healthcare: A Web-Microservices System Ready for Chest Pathology Detection 12th

Deep Learning for Healthcare: A Web-Microservices System Rea...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Quevedo, Sebastian Behzadi-Khormouji, Hamed Dominguez, Federico Pelaez, Enrique ESPOL Univ Elect & Comp Engn Guayaquil Ecuador Univ Antwerp Imec IDLab Antwerp Belgium Univ Catolica Cuenca XR LAB Cuenca Ecuador

ISBN: (纸本)9783031602177;9783031602184

The automation of medical diagnosis has accelerated thanks to the integration of artificial intelligence (AI), particularly in interpreting pathologies in chest X-rays. This study presents a web microservices system that uses a deep learning model to classify thoracic pathologies. The system improves clinical decision-making by providing visual aids, including heat maps for model explainability and a comprehensive set of medical image manipulation tools. The back-end, developed using a microservices architecture, ensures robust data management, secure user authentication, and efficient AI model integration. The results highlight the system's accuracy in detecting pathologies with an average AUC of 0.89, an easy-to-use interface, and the transformative impact of AI explainability in clinical settings.

关键词： Deep Learning Chest X-rays AI Explainability Healthcare Technology Microservices Architecture

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Enhancing Battery State of Charge Estimation Through a Combination of Extended Kalman Filter and Artificial Neural Network 4th

Enhancing Battery State of Charge Estimation Through a Combi...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Lehmam, Oumayma Kharbach, Jaouad El Fallah, Saad Masrour, Rachid Rezzouk, Abdellah Jamil, Mohammed Ouazzani Univ Sidi Mohamed Ben Abdellah Fac Sci Dhar El Mahraz Lab Phys Solide BP 1796 Fes 30003 Morocco Univ Privee Fes Lab Syst & Environm Durables Lot Quaraouiyine Route Ain Chkef Fes 30040 Morocco

ISBN: (纸本)9783031686528;9783031686535

In the field of battery management and state estimation, accurately determining the initial state of charge (SOC) is a necessary yet difficult task. The Extended Kalman Filter (EKF) is an effective technique for estimating the battery state of charge;nevertheless, its accuracy can be considerably impacted by the initial battery charge state (initSOC), especially if it differs significantly from the real state of charge. This disparity has the potential to cause problems such as sluggish convergence or, in more extreme cases, full divergence in the estimation process. To solve this, we propose an innovative approach in this paper that uses 1D Convolutional Neural networks (1D CNN) to improve the EKF's initial state estimate.

关键词： Li-ion battery State of Charge Battery modeling Battery Management System Extended Kalman filter Electrical vehicle 1D Convolutional Neural Network

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Enhancing Network Intrusion Detection systems Through Dimensionality Reduction 6th

Enhancing Network Intrusion Detection Systems Through Dimens...

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6th International Conference on Soft Computing and Data Mining (SCDM)

作者： Abualhaj, Mosleh M. Al-Khatib, Sumaya N. Al-Allawee, Ali Munther, Alhamza Anbar, Mohammed Al Ahliyya Amman Univ Dept Networks & Cybersecur Amman 19328 Jordan Univ Haute Alsace IRIMAS Lab F-68000 Colmar France Univ Technol & Appl Sci Sur 411 Muscat Oman Univ Sains Malaysia Natl Adv Ctr IPv6 Gelugor 11700 Penang Malaysia

ISBN: (纸本)9783031669644;9783031669651

The proliferation of data on the Internet has intensified the difficulties faced by network intrusion detection systems (NIDS) in handling large dimensions of data that include irrelevant and duplicate elements. This process entails a significant investment of time and effort in accurately identifying the attack while also experiencing a rise in false alarms. Applying dimensionality reduction may solve this problem. This paper employs and compares two feature selection methods, the Whale Optimization Algorithm (WOA) and Hawks Optimization (HHO). These methods reduce the number of features by finding the minimum number without affecting the performance of the NIDS system. The main idea is to select the key features from the NSL-KDD datasets using WOA and HHO. The results have shown a better performance of the NIDS system when using HHO rather than WOA with binary classification. The HHO and WOA achieved 93.83% and 92.83% accuracy, respectively. These results were achieved using the Support Vector Machine Classifier (SVM).

关键词： Feature selection WOA HHO NIDS SVM

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Late Leaf Spot Detection and Its Effect on Pod Quality of Groundnut Plants Using Deep Neural networks: A Review 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Gadagkar, Akhilesh Kanakaraddi, Suvarna Kalwad, Priyadarshini Motagi, B. N. Chikaraddi, Ashok KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India UAS MARS AICRPG Dharwad Karnataka India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Late Leaf Spot [LLS] [1], caused by various pathogens such as fungi and bacteria, is a significant foliar disease that affects groundnut (Arachis hypogaea) plants, impacting both agricultural productivity and food security. This survey aims to provide a comprehensive overview of the state-of-the-art research on late leaf spot detection and its subsequent effects on the quality of groundnut pods. The study establishes the importance of groundnut cultivation and emphasizes the relevance of understanding late leaf spot's impact on pod quality. Various techniques ranging from visual observation to sophisticated image processing [2] are evaluated in terms of their accuracy, efficiency, and applicability. The study further delves into the implications of late leaf spot on pod quality. Drawing from past studies, evidence is presented on the disease's adverse effects on pod size, weight, oil content, and nutritional value. The correlations between disease severity and pod quality deterioration are examined, highlighting the potential repercussions for groundnut-based industries and nutrition security. This survey not only underscores the significance of understanding the relationship between late leaf spot and pod quality but also addresses the broader implications for agricultural practices and decision-making. The potential economic, environmental, and societal consequences of disease management strategies are considered, underscoring the need for sustainable approaches.

关键词： Groundnut Late leaf spot Image processing Pod quality Convolutional neural network (CNN) Deep neural network

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