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International ISC Conference on information Security and Cryptology (ISCISC)

作者： Fatemeh Tahmasbi Neda Moghim Mojtaba Mahdavi Department of Computer engineering - Information Technology Faculty University of Isfahan Isfahan Iran

Covert channel is one of the remarkable issues which are raised in computer network security. These channels use the computer network's resources to transmit information furtively. Since the purpose of covert channels is to stay hidden from the network security equipments, hence their detection will be difficult. The growth of wireless networks and their broadcast nature make them suitable environments for covert channels' establishment. This study proposes a method to create a covert channel in local area wireless networks by using the temporal characteristics of CSMA/CD algorithm and manipulating the transmission pattern. To increase the bandwidth, hidden data is considered ternary. To assess the covertness of the proposed covert channel, two-sample Kolmogorov-Smirnov test is used. The proposed covert channel achieves the bandwidth of 1620 bps with a high level of security.

关键词： Receivers Timing Wireless networks Transmitters Security Histograms

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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Comparison of representation switching number and achieved bit-rate in DASH algorithms

Comparison of representation switching number and achieved b...

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International Conference on Smart Systems and Technologies (SST)

作者： Jelena Vlaović Drago Žagar Snježana Rimac-Drlje Luka Filipović Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781728197593

ISBN: (纸本)9781728197609

MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is adaptive streaming standard developed to unify previously developed adaptive streaming technologies. Over the years various MPEG DASH based adaptive streaming algorithms have been developed, mainly hybrid algorithms that take into account the buffer occupancy, available bandwidth and other parameters like video segment size. Adaptive streaming algorithms are used to determine the next video representation in order to ensure the best possible quality of experience in varying network conditions. They must balance well between the initial video delay and the number and depth of representation switching, as well as avoid video stalling events. This paper compares three different algorithms: Rate Adaptation Algorithm (LIU), Segment-Aware Rate Adaptation (SARA) and Content-Aware Rate Adaptation (CARA) in five network test cases with three video sequences. Obtained results show that on average LIU has the most representation switching events regardless of the spatial (SI) and temporal information (TI) of video sequence and it has the lowest achieved bitrate values. Although CARA and SARA achieve similar results, due to the fact that SARA has four phases of deciding on the representation of the next segment, it has the least number of switching events and a larger value of switching depth. CARA achieves highest average values of achieved bitrate in cases when video sequences Elephants Dream and Big Buck Bunny with lower SI and TI are used.

关键词： Switches Video sequences Streaming media Bit rate Quality of experience Bandwidth Encoding

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Pathfinding in Procedurally Generated Mazes

Pathfinding in Procedurally Generated Mazes

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International Conference on Smart Systems and Technologies (SST)

作者： Matej Kuprešak Časlav Livada Tomislav Galba Alfonzo Baumgartner Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

This paper presents the development of a simulator for procedural generation of mazes and evaluation of pathfinding algorithms. The implementation includes a Perlin noise-based procedural generation where the generated noise determines the colors of the terrain, increasing the realism of the terrain. The Unity interface allows the user to control and change these properties. Two pathfinding algorithms, namely Dijkstra’s algorithm and the breadth-first search algorithm, are used to test and analyze their effectiveness. The algorithms use positional data to identify and display feasible paths between specific points. The integrated user interface provides a straightforward platform to experiment with the algorithms and visualize their results, including the number of nodes in the path and the duration of pathfinding. In addition, a graphical representation allows real-time tracking of the player’s movement along the selected path. The results presented provide valuable insights for selecting algorithms for specific scenarios. Overall, this simulator is a valuable tool for researchers and developers involved in maze generation and pathfinding algorithm evaluation.

关键词： Procedural generation Image color analysis Tracking Heuristic algorithms Visual effects Smart systems Search problems Real-time systems Reliability Testing

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Influence of Segmentation Parameters on Video Quality in Dynamic Adaptive Streaming

Influence of Segmentation Parameters on Video Quality in Dyn...

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International Symposium on Electronics in Marine (ELMAR)

作者： Jelena Vlaović Snježana Rimac-Drlje Drago Žagar Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781728159737

ISBN: (纸本)9781728162058

Dynamic adaptive streaming has been evolving during the last decade in order to meet high expectations regarding video quality of streamed video sequences on various devices. Every year various dynamic adaptation algorithms that try to improve user quality of experience are developed. Another important aspect of adaptive streaming is the video segmentation process as well as the dataset used for testing purposes. The goal of the research presented in this paper was to improve the segmentation process. Structural Similarity Index (SSIM) was used to determine coding bitrates where the overlapping of video quality for different spatial resolutions occur in order to specify proper representations for the segmentation. Two video sequences with different spatial and temporal activity were encoded based on selected representations and tested using the Basic adaptation algorithm (BAA) and Segment-Aware Rate Adaptation (SARA). Compared to previously available segmentation, test results show improvement in SSIM values especially for a video sequence with high spatial and temporal activity.

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Conceptual Framework for Knowledge Flow in Software Development Process

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Research Journal of Applied Sciences, engineering and technology 2017年第12期14卷 450-457页

作者： Marzanah A. Jabar Mustafa S. Khalefa Rusli Hj. Abdulla Salfarina Abdulla Department of Software Engineering and Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM) 43400 Selangor Serdang Malaysia

The main aim of this study to merge knowledge flow activities with software development process Past models of flow of knowledge lacked in terms of software development and lacked on sharing right knowledge and lacked in-depth exploration of context modeling, which made those models less applicable. In addition now it attracts much consideration, attention in the knowledge management field. In a knowledge-based organization, knowledge workers need to acquire a variety of knowledge (information) about their tasks. Therefore; many organizations have built knowledge support plat forms to assist workers in meeting their knowledge-needs. These platforms help workers to identify and share knowledge in order to speed up organization innovation and improve employee Productivity. This research proposes a conceptual framework of context oriented flow of knowledge in software development aspects. In this framework, the context is seen as an inseparable element of flow of knowledge, which is regarding the creation, transformation as well as application of knowledge items. One of the main challenges is how to exploring knowledge flow, sharing knowledge in software development process. The main goal for this research to provide a framework to solve this challenge. In this research used questionnaire instrument with 21 people as they are from software development process environment, also in this study want to improve all factors, task, type of knowledge, nature of knowledge used in software development process during the flow of knowledge. Finally in this study validate the framework using experts review, 4 experts from academic and industrial result was great shows all phases in software development process and factors when active, moreover in this research verified the component by using cronpach alpha methods the result was good in acceptable rang 0.711.

关键词： Artifacts knowledge flow knowledge flow element knowledge flow with software process roles software development process

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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Harmful Brain Activity Classification Using Ensemble Deep Learning

Harmful Brain Activity Classification Using Ensemble Deep Le...

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International Symposium on Electronics in Marine (ELMAR)

作者： Marija Habijan Robert Šojo Ivana Hartmann Tolić Irena Galić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350375428

ISBN: (纸本)9798350375435

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions.

关键词： Accuracy Refining Pattern classification Predictive models Brain modeling Electroencephalography Robustness Ensemble learning Spectrogram Testing

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An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

An Efficient Method for License Plate Localization Using Mul...

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Conference on Artificial Intelligence and Robotics and Asia-Pacific International Symposium

作者： Mahdi Rezaei Morteza Isehaghi Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Iran

ISBN: (纸本)9781728128436

Accurate license plate localization is the most important prerequisite in ANPR (Automatic Number Plate Recognition) systems. Majority of the existing algorithms use a single feature to obtain the license plate location which causes to potential false detections. In this article we propose a robust methodology using 16 statistical features while we still preserve real-time processing of the system which is a requirement for such applications. The proposed method uses a Vertical Projection technique and Discrete Fourier Transform (DFT) in order to extract multiple statistical features, as well as K-means clustering and multilayer perceptron neural network technique to identify the location of a license plate in an image. The method is compared with the state-of-the-art research in the field and the effectiveness of the method is evaluated for various types of license plates with different scripts.

关键词： Licenses Discrete Fourier transforms Feature extraction Neural networks Genetic algorithms Training Image color analysis

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