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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811935749

the proceedings contain 149 papers. the special focus in this conference is on Information and Communication Technology for Intelligent Systems. the topics include: COVID-19 Triggers a Paradigm Shift in Technology for Insurance Industry;internet of things in Saudi Arabia Universities: State of the Art, Future Opportunities, and Open Challenges;study of Security in Cryptocurrency;study and Analysis of Network Steganography Methods;preface;Review of Different ML Techniques Used for Heart Disease Prediction;survey on Driver Fatigue Detection Using Sensors, Big data analytics and machine learning Techniques;on Characteristics of κ–Q–Fuzzy Translation and κ–Q–Multiplication in T-Ideal in T-Algebras;blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research;a Future to the Blockchain Technology and Its Concepts;Impacts of DDoS Attacks in Software-Defined Networks;ioT-Based Agriculture Trend Prediction Based on Weather data;EEG in Optic Nerves Disorder Based on FSVM Using Kernel Membership Function;DDoS Detection in ONOS SDN Controller Using Snort;classification of Keratoconus Using Corneal Topography Pattern with Transfer learning Approach;intelligent Heuristic Keyword-Based Search Methodologies applied to Cryptographic Cloud Environment;image Enhancement by Various Segmentation Techniques Using machine learning;Surakhsha Kavach: ML-Based Cross-platform Application for COVID-19 Vulnerability Detection;Malware Family Categorization Using Genetic Algorithm-CNN-Based Image Classification Technique;master data Management Maturity Evaluation: A Case Study in Educational Institute;malware Family Classification Using Music Information Retrieval Techniques;text-Based Prediction of Heart Disease Doctor Chatbot Using machine learning;intelligent Proctoring System;project-Based learning: A Contemporary Approach to Blend theory and Practical Knowledge of database Management Course;student’s Employability Concern for Colleges and Universities.

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Managerial Budget Improvements Using data analytics 6

Managerial Budget Improvements Using Data Analytics

引用

6th international conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Pushkarna, Shubhi Walcott, Terry H. University of Wales Trinity Saint David School of Applied Computing Swansea United Kingdom

ISBN: (纸本)9798350340921

Covid-19 has created a series of unknowns that have afforded organisations, large and small, with an opportunity to appraise their spending and general budgeting practices. In so doing, the average consumer is now impacted in such a manner that improvements in terms of addressing budget shortcomings are now paramount to any form of success. Henceforth, the budget management learning curve does require organisations generally to manage finances and staffing demands equally. Owing to this the speed of engagement is heightened and a measured approach for arriving at solid results are necessitated. Henceforth, this study will demonstrate the need to comprehend budgets and align each to the core business processes of a given organisation. Having done so, a schema of approaches underpinned by data analytics can be harnessed for addressing the deficiencies faced in businesses generally. To ensure that the needs of most businesses can be met, this paper will also demonstrate fundamental business drivers for collating business expenses and business projections from historical records. In so doing, results garnered have indicated that contemporary methodologies associated to data analytics;overlooked budgeting areas are highlighted;business growth opportunities are initiated;and evidential suggestions and budget optimization situations are considered by all businesses and consumers. © 2023 IEEE.

关键词： Budget control

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Sensitive Path oriented Malicious data Generation for Web applications 6

Sensitive Path oriented Malicious data Generation for Web ap...

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6th international conference on data Science and Information Technology, DSIT 2023

作者： Wang, Linxin Wang, Weiwei The Third Research Institute of Ministry of Public Security Shanghai China Beijing University of Chemical Technology Dept. of Computer Science and Technology Beijing China

ISBN: (纸本)9798350304442

SQL injection always threatens the security of web applications. Static analysis and taint analysis are widely used to find vulnerable code paths, called sensitive paths, to detect SQL injection vulnerabilities. But false positives inevitably exists. To alleviate this problem, researchers try to generate test cases to cover sensitive paths. But it can only validate the feasibility of sensitive paths. this paper proposes a sensitive path-oriented malicious data generation to expose SQL injection vulnerabilities of sensitive paths on web applications. In more detail, taint analysis, genetic algorithm, and machine learning are integrated to generate malicious data to reveal SQL injection automatically. Taint analysis is used to find sensitive paths for Web applications. Based on sensitive paths' historical attacked results by malicious data, a predictive model is constructed by machine learning to distinguish what malicious data is more likely to detect SQL injection vulnerability. A genetic algorithm is employed with the predictive model to generate malicious data to detect SQL injection vulnerability. the experiments are conducted on five open source web applications, and the results demonstrate that our approach is more efficient in malicious data generation for SQL injection vulnerability detection. © 2023 IEEE.

关键词： Genetic algorithms

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2023 international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

2023 International Conference on Cloud Computing and Artific...

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6th IEEE international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2023

ISBN: (纸本)9798350303063

the proceedings contain 36 papers. the topics discussed include: towards an IoT architecture based on machine learning for missing data prediction on the edge;a survey on recent intrusion detection systems in cloud environment;advancing breast cancer diagnosis with machine learning: exploring data balancing, feature selection, and Bayesian optimization;using serverless FaaS for invoking long-running jobs;security governance in IOT environment: a state of art;data-driven approach for residential occupancy modeling using PIR sensors: a Moroccan case study;attempts in worst-case optimal joins on relational data systems: a literature survey;assessing digital government maturity model for developed countries. application to Singapore;evaluation and analysis of network safety mechanisms in SDN infrastructure;comparison of a FaaS- and SaaS-based ETL process in a cloud environment;and sentiment analysis applied to Arabic tweets using machine learning and deep learning.

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A Continual learning System with Self Domain Shift Adaptation for Fake News Detection 10

A Continual Learning System with Self Domain Shift Adaptatio...

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10th IEEE international conference on data Science and Advanced analytics, DSAA 2023

作者： Basterrech, Sebastian Kasprzak, Andrzej Platos, Jan Wozniak, Michal Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350345032

Detecting fake news is currently one of the critical challenges facing modern societies. the problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. this paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021-COVID19 Fake News Detection). the proposed learning system works in a Continual learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. the method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. the performance of the approach has been evaluated over the generated data stream. the convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant. © 2023 IEEE.

关键词： Long short-term memory

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ML threat Detection with KDD Cup data 6

ML Threat Detection with KDD Cup Data

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6th IEEE international conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Jeridi, Mohamed Hechmi Azzabi, Tarek Amor, Nada Ben Boudabous, Emna Tunisian Naval Academy Menzel Bourguiba7050 Tunisia Tunisian Naval Academy Laboratory LR-TMSN Menzel Bourguiba7050 Tunisia Enit National Engineering School of Tunis Tunisia

ISBN: (纸本)9798350321029

Artificial Intelligence (AI) has rapidly emerged as a transformative technology, with applications in numerous fields including security. With the increasing frequency and sophistication of security threats, such as cyber-attacks and malware, the need for effective methods to detect and prevent these threats is critical. machine learning (ML), a subfield of AI, has been applied in this context, as its ability to analyze large amounts of data and identify patterns can be particularly useful in detecting security threats. In this empirical study, we examine the use of machine learning techniques for security threat detection, with the goal of advancing the understanding of the most effective approaches and techniques. © 2023 IEEE.

关键词： data mining

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Using machine learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old 1

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16th international conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th international conference on EUropean Transnational Education, ICEUTE 2023

作者： Sáiz-Manzanares, María Consuelo Burgos University Facultad de Ciencias de La Salud Paseo de Comendadores s/n Burgos Burgos09001 Spain

the use of machine learning techniques and technological resources that facilitate diagnosis and intervention at early ages (0–660;years) will facilitate the development of both processes from the point of view of... 详细信息

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

the use of machine learning techniques and technological resources that facilitate diagnosis and intervention at early ages (0–6 years) will facilitate the development of both processes from the point of view of accuracy. this paper analyses the most useful machine learning techniques to be applied to diagnosis and therapeutic intervention in the field of early care. It also describes the development of a web application, eEarlyCare, which includes the recording and interpretation of the results through learning analytics techniques of the observation of different early development problems. In addition, an evaluation of the usability of this web application is carried out as part of a training project aimed at updating the technological and data analysis strategies of early intervention professionals. the results support the use of this type of computer applications in which learning analytics and visualisation of results techniques are included. the proposals for improvement focus on the use of technological resources similar to intelligent voice assistants that regulate the work of the therapy professional. Further studies will address these proposals for improvement. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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A Recent Study of machine learning Based Techniques for the Detection of Cyber-Attacks on Web Applications 6

A Recent Study of Machine Learning Based Techniques for the ...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Sushant Mahajan, Makul Batra, Salil School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350304480

Detecting cyberattacks remains a challenging task in almost every digital environment, particularly when dealing with passive attacks. Major organizations have suffered from financial losses and negative publicity due to the lack of timely detection of ongoing intrusions. According to IBM analytics 2022, 83% of companies are at risk of falling victim to cyberattacks. this can happen at any time, and the shocking fact is that in 2022, just in the United States, the typical expense of a data breach was nearly 9.5 million US dollars. this paper provides a comparative analysis of researchers'methodologies and architectures proposed in recent years to detect and mitigate this issue. Furthermore, the significance of machine learning (ML) is emphasized in this paper to enhance existing system security. the review paper covers Denial of Service/Distributed Denial of Service (DoS/DDoS), cross-site scripting (XSS), SQL injection, and other network layer attacks. A total of 20 papers were compared based on the attack covered, performance, collected dataset, and the technique used in the proposed model. © 2023 IEEE.

关键词： cyberattacks Cybersecurity DoS/DDoS machine learning phishing SQLI web-application attack detection XSS

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6th international conference on Information and Knowledge Systems, ICIKS 2023

6th International Conference on Information and Knowledge Sy...

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6th international conference on Information and Knowledge Systems, ICIKS 2023

ISBN: (纸本)9783031516634

the proceedings contain 24 papers. the special focus in this conference is on Information and Knowledge Systems. the topics include: Exploiting machine learning Technique for Attack Detection in Intrusion Detection System (IDS) Based on Protocol;robust Aggregation Function in Federated learning;should I Share or Should I Go?: A Study of Tacit Knowledge Sharing Behaviors in Extended Enterprises;designing a User Contextual Profile Ontology: A Focus on the Vehicle Sales Domain;Management of Implicit Ontology Changes Generated by Non-conservative JSON Instance Updates in the τJOWL Environment;a Model Driven Architecture Approach for Implementing Sensitive Business Processes;Extension of the Functional Dimension of BPMN Based on MDA Approach for Sensitive Business Processes Execution;inclusive Mobile Health System for Yoruba Race in Nigeria;epistemology for Cyber Security: A Controlled Natural Language Approach;decision and Information Support System for a Framework to Building Multicriteria Decision Models;Application of Fuzzy Decision Support Systems in IT Industry Functioning;moving Towards Explainable Artificial Intelligence Using Fuzzy Rule-Based Networks in Decision-Making Process;Sentiment Analysis: Effect of Combining BERT as an Embedding Technique with CNN Model for Tunisian Dialect;an Enhanced machine learning-Based Analysis of Teaching and learning Process for Higher Education System;topic Modelling of Legal Texts Using Bidirectional Encoder Representations from Sentence Transformers;graph Representation learning for Recommendation Systems: A Short Review;FITradeoff Decision Support System applied to Solve a Supplier Selection Problem;a Step-By-Step Decision Process for Application Migration to Cloud-Native Architecture;ACTIVE SMOTE for Imbalanced Medical data Classification;Evolutionary Graph-Clustering vs Evolutionary Cluster-Detection Approaches for Community Identification in PPI Networks;predictive Monitoring of Business Process Execution Delays.

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Improving Inertial Sensor-based Human Activity Recognition using Ensemble Deep learning 8

Improving Inertial Sensor-based Human Activity Recognition u...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Rojanavasu, Pornthep Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

Sensor-based human activity recognition (S-HAR) is a famous study focusing on detecting human physiological actions by interpreting various sensors, especially one-dimensional time series information. Typically, S-HAR machine learning methods were developed using handcrafted characteristics. Unfortunately, this is a complicated process that involves feature engineering and a high level of domain knowledge. Due to the development of deep neural networks, classification techniques could efficiently handle relevant characteristics from raw sensor data, resulting in enhanced classification outcomes. In this study, we describe a unique method for S-HAR based on ensemble deep learning with sensor nodes connected to the waist, chest, leg, and arm. Implementing and training three deep learning networks is performed using a publically available dataset, including wearable sensors from eight human actions. the findings demonstrate that the proposed Ens-ResNeXt model provides the maximum accuracy and F1-score, which is superior to existing techniques. © 2023 IEEE.

关键词： learning systems

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