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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Ahmed Baber Faizan Ahmed Attique Ur Rehman Sabeen Javaid Menwa Alshammeri Azka Mir Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science College of Computer and Information Sciences Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies.

关键词： Heart Legged locomotion Accuracy Fluids Machine learning algorithms Pumps Nearest neighbor methods Cardiovascular diseases Random forests Blood

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A Two-stage Anomaly Detection System for Fog Computing Services

A Two-stage Anomaly Detection System for Fog Computing Servi...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Sunday Oyinlola Ogundoyin Ismaila Adeniyi Kamil Isaac Adewale Ojedokun John Oluwaseun Babalola Vincent Omollo Nyangaresi Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10 −5 . The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively.

关键词： Performance evaluation Cloud computing Accuracy Computational modeling Mission critical systems Machine learning Time factors Long short term memory Anomaly detection Edge computing

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Adaptive Image Steganography: A Review and Research Directions*

Adaptive Image Steganography: A Review and Research Directio...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Ayyah Abdulhafith Mahmoud Fadhl Hanein Omar Mohamed Shareif Malak Ahmed Elsherif Ali Abdullah Mohammad Abdullah Elmangoush Artificial Intelligence Department Libyan International University Benghazi Libya Software Engineering Department Libyan International University Benghazi Libya Telecommunication Department Libyan International University Benghazi Libya Computer Network Department Libyan International University Benghazi Libya

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Steganography involves concealing secret information in an appropriate multimedia carrier, such as text, image, audio, and video files, in such a way that only the sender and receiver suspect the existence of the secret information. Images are the most popular multimedia carriers. Adaptive image steganography is a promising new trend in the steganographic field. It basically means that the embedding locations are altered adaptively depending on the image content. The primary objective of this paper is to investigate and discuss various approaches to adaptive image steganography. The approaches can be broadly categorized as CNN-based, end_to_end GAN-based, and minimizing additive distortion function-based, both huristically and automatically designed. In addition to discussing the four categorized approaches, this paper provides an overview of image steganography including its characteristics or properties, and evaluation metrics. A summary table of all the details is also provided for convenience. The purpose of this paper is to assist fellow researchers by compiling current trends, their advantages and disadvantages, and identifying improvement gaps

关键词： Training Steganography Additives Reviews Streaming media Market research Generative adversarial networks

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication

Advancements in 5G and 6G Technologies Shaping the Future of...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Gouthami Kompally Lakshmi Narasimhan Srinivasagopalan Rohidas Balu Sangore Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Department of CSE-IOT Malla Reddy Egineering College(A) India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering MIT Art Design Technology University Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems.

关键词： 6G mobile communication Ethics Technological innovation Computational modeling Medical services Energy efficiency Real-time systems Security Resource management Standards

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On Retrofitting Provenance for Transparent and Fair software - Drivers and Challenges

On Retrofitting Provenance for Transparent and Fair Software...

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Workshop on software Fairness (FairWare)

作者： Jens Dietrich Matthias Galster Markus Luczak-Roesch School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand School of Information Management Victoria University of Wellington Wellington New Zealand

There have been ongoing discussions about how to ensure transparency and fairness in software that utilise artificial intelligence (AI). However, transparency and fairness are not limited to AI. Modern (non-AI) software is often constructed in a black box fashion. This means, components and services provide some functionality, but details on how this is achieved are hidden. Common software development and design principles like encapsulation and information hiding promote this. Engineers often only look inside the black boxes when they need to fix problems, e.g., when tracing bugs or vulnerabilities. The demand for transparency has created a need to open those black boxes also to non-engineers. For instance, businesses need to demonstrate regulation compliance, and end users want to understand how systems make fair decisions that affect them. However, adding provenance (i.e., the ability to gather information about data and algorithms used in systems) to existing systems is invasive and costly, and current approaches to collect provenance data are not designed to expose data to end users. We argue that this requires “provenance retrofitting”, i.e., adding provenance capabilities to systems mechanically, and exposing provenance data through standard language and service application programming interfaces (APIs). This could facilitate an infrastructure that supports transparency, which then can in turn be used to create feedback mechanisms for users that in the long term can improve the fairness of software. In this paper we discuss drivers, objectives, key challenges and some possible approaches to provenance retrofitting.

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A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing

arXiv

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arXiv 2023年

作者： Zhou, Yu Chen, Yu Zhang, Xiao Lai, Pan Huang, Lei Jiang, Jianmin The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Computer Science South-Central Minzu University Wuhan430074 China The College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Recently, deep learning-based compressed sensing (CS) has achieved great success in reducing the sampling and computational cost of sensing systems and improving the reconstruction quality. These approaches, however, largely overlook the issue of the computational cost;they rely on complex structures and task-specific operator designs, resulting in extensive storage and high energy consumption in CS imaging systems. In this paper, we propose a lightweight but effective deep neural network based on recurrent learning to achieve a sustainable CS system;it requires a smaller number of parameters but obtains high-quality reconstructions. Specifically, our proposed network consists of an initial reconstruction sub-network and a residual reconstruction sub-network. While the initial reconstruction sub-network has a hierarchical structure to progressively recover the image, reducing the number of parameters, the residual reconstruction sub-network facilitates recurrent residual feature extraction via recurrent learning to perform both feature fusion and deep reconstructions across different scales. In addition, we also demonstrate that, after the initial reconstruction, feature maps with reduced sizes are sufficient to recover the residual information, and thus we achieved a significant reduction in the amount of memory required. Extensive experiments illustrate that our proposed model can achieve a better reconstruction quality than existing state-of-the-art CS algorithms, and it also has a smaller number of network parameters than these algorithms. Our source codes are available at: https://***/C66YU/CSRN. © 2023, CC BY-NC-SA.

关键词： Compressed sensing

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Comparative analysis of machine learning algorithms for air quality index prediction

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Machine Learning for Computational Science and engineering 2025年第1期1卷 1-20页

作者： Desai, Tanay Kapadia, Shivam Halani, Mahir Zinzuwadia, Parth Shah, Kanish Shah, Manan Prajapati, Mitul Atul Vidyalaya Valsad India The Riverside School Ahmedabad India Department of Computer Science School of Arts and Sciences Ahmedabad University Gujarat India Software Engineer at Walmart Global Tech Sunnyvale USA Department of Chemical Engineering Pandit Deendayal Energy University Gandhinagar India S S Agrawal Institute of Engineering & Technology Devina Park Society Navsari India

In several urban industrial regions affected by air pollution, it is crucial to monitor air quality in order to improve the quality of life and prevent any damage to health. This paper mainly focuses on the prediction of air quality index (AQI) using two different machine learning algorithms SVM and KNN. In recent times, machine learning has become widely popular and relevant in the forecasting of AQI because of its ability to work with large datasets and provide highly accurate conclusions from raw data. This study helps in analyzing several international research works which helps in better understanding of the forecasting power of algorithms used and the predicted outcomes provide insights into the air quality which aid authorities in decision making. Our analysis shows that hybrid models, created using multiple algorithms, outperform traditional models involving a single algorithm. Moreover, the usage of larger datasets to train the models led to more accurate results.

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Discovering Authorization Business Rules toward Detecting Web Applications Logic Flaws

Discovering Authorization Business Rules toward Detecting We...

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Information Technology (ACIT)

作者： Hamza Alkofahi David Umphress Heba Alawneh Department of Software Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Auburn University Auburn USA Department of Cybersecurity Al Hussein Technical University Amman Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Aggressive integration of validation checks into web framework software has altered the attack surface of web applications by reducing the opportunity for traditional injection flaws. The hacking community's reaction has shifted to a more subtle - and more challenging to detect - form of attacks, that of discovering and exploiting underlying application business logic. The lack of accurate business rules defining the final application product extends its logical vulnerability surface. We propose a novel black-box approach for discovering authorization business rules in web applications through users' dynamic behavior. Allowing applications that lack formal specifications to be better tested for logic vulnerabilities. Our approach discovers groups using agglomerative hierarchical clustering based on different profiling techniques that capture users' actions and privileges. We also automated the process of identifying the optimal number of roles. The results indicated high quality and stability in discovering business rules, even in smaller datasets.

关键词： Authorization Closed box Behavioral sciences Formal specifications Data mining Information technology computer crime

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A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition

arXiv

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arXiv 2024年

作者： Khurma, Ruba Abu Alhenawi, Esraa Braik, Malik Hashim, Fatma A. Chhabra, Amit Castillo, Pedro A. MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Computer Science Department Al- Ahliyya Amman University Amman Jordan Software Engineering Department Al- Ahliyya Amman University Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Faculty of Engineering Helwan University Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, that is, they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms (MAs) as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, S

关键词： Evolutionary algorithms

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Information and Intelligent Forecasting Systems Based on the Methods of Neural Network Theory

Information and Intelligent Forecasting Systems Based on the...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Yuliia Kostiuk Olena Kryvoruchko Alona Desyatko Yuliia Samoilenko Kateryna Stepashkina Rostislav Zakharov Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyiv Ukraine Department of automation and computer technologies of control systems National University of Food Technologies Kyiv Ukraine

This paper is dedicated to the problem of the research of the information and intelligent forecasting systems which solve the problems of regression analysis. This class of the systems includes the systems that use different mathematical apparatus for forecasting, for the implementation of which a wide range of methods and algorithms is used. The paper also deals with the problems of regression analysis using elements of neural network theory. Developing a set of functions to form an approximating function allows you to create models that work efficiently. In existing information and intelligence systems, either implemented functions adapted to solve specific forecasting tasks, or, as in the case of neuropackages, it is considered that the proposed functions can be used to solve a wide range of tasks, adaptation to the conditions of the task occurs during the design of the neural network architecture. To create an approximating function, it is possible to implement linear and non-linear functions, which have proven themselves well in neural network analysis for solving a wide range of tasks and are obtained within the framework of deterministic chaos theory. To implement the function that approximates the dependence of the predicted value on the forecast arguments, the use of a two-layer heterogeneous, artificial neural network without feedback is proposed.

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