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International Conference on computer and Applications (ICCA)

作者： Wijesinghe W.M.A. J Abeysinghe Y.P. P Rukshana M.A. F Adhikari A.M.P. S Kavinga Yapa Abeywardhana Deemantha Siriwardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Employers should recognize that employees are the most vulnerable aspect of business environments since these cyber hazards are growing because of user neglect, lack of fundamental security discipline, and a fast-changing threat context. The theoretical approach proposed in the study bridges the difference by carefully following and handling the staff's internet presence. Overall, the study describes a comprehensive solution to govern the security of employees' internet presence by utilizing Artificial Neural Networks (ANN) for advanced intrusion detection, Natural Language Processing (NLP) for implementation and enforcement of browser-based security policies, visual and text data recognition to identify phishing attempts, and lastly analyzing and profiling users based on their online presence via a mathematical model. As a result, this study provides a comprehensive solution for mitigating risks related to human factors in corporate environments.

关键词： Visualization Text recognition computer hacking Phishing Intrusion detection Artificial neural networks Human factors Natural language processing Mathematical models Hazards

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Research and development of two-factor authentication methods using neural networks 4

Research and development of two-factor authentication method...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Darbek, Temirkhan Mukasheva, Assel Bissembayev, Alibek Gnatyuk, Sergiy Mukammejanova, Dinargul The Department of Information Systems and Cybersecurity Almaty university of power engineering and telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The School of Information Technology and Engineering Kazakh British Technical University Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering at National Aviation University National Aviation University Kyiv Ukraine The Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (纸本)9798350374865

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital. © 2024 IEEE.

关键词： biometric authentication deep learning digital security identification Neural networks

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Enhancing Agricultural Efficiency with IoT-Enabled Precision Irrigation Management Using LoRaWAN technology

Enhancing Agricultural Efficiency with IoT-Enabled Precision...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： J.A.S.K. Jayasinghe T.M.H.L. Thennakoon U.G.C. Ruchira N.G.C.L. Wimalasena J.A.D.C.A. Jayakody Dinithi Pandithage Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

The Internet of Things-empowered precision irrigation management system with LoRaWAN technology is presented given the growing food requirements across the world and pressing calls for judicious use of water in agriculture. Flow Pilot, specifically designed for chili farms, optimizes water application while enhancing crop health using precise and automated irrigation. The Flow Pilot features advanced sensors such as soil moisture, temperature, humidity, and rain sensors that collect real-time environmental data transmitted over a LoRaWAN network for analysis. Flow Pilot uses the Machine learning algorithm by analyzing sensor data to make localized irrigation recommendations based on crop needs, environmental factors, and weather forecasting. LoRaWAN gateways will support this for long-ranged communications over power-consuming agricultural fields. Besides this, it can use different network architectures. There is also an automated water tank management module that regulates the supply of water and pH levels to have optimal conditions for crop growth. A visually simple dashboard interface enables farmers to inspect and operate irrigation processes remotely, hence rendering the whole process far more efficient. Finally, over the extensive period of field tests executed by the project, Flow Pilot was able to save water, improve crop yields, and help support sustainable agriculture. For example, this testing supports the potential of IoT with LoRaWAN technologies, such as the Flow Pilot implementation, for a game change in conventional irrigation practices into a modern, scalable, and reliable solution.

关键词： Temperature sensors Water Irrigation Storage management LoRaWAN Machine learning Nutrients Real-time systems Sensor systems Crop yield

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Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages 11th

Trans-Compiler-Based Database Code Conversion Model for Nati...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Barakat, Rameez Radwan, Moataz-Bellah A. Medhat, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of computers and artificial intelligence Benha University Banha Egypt

ISBN: (纸本)9783031215940

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile applications, this paper proposes a new database code conversion model based on trans-compilation and pattern matching. It proposes a model that can be used to support database code conversion between native languages and platforms and applies the proposed model to support the conversion of Firebase Firestore database code from Android (Java) to iOS (Swift) using TCAIOSC. The enhanced tool’s results show high accuracy for the converted database code and a noticeable improvement in the overall conversion results for TCAIOSC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pattern matching

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Teaching in student-centred active learning spaces: How relational, pedagogical, spatial, and technological aspects intertwine and affect the learning environment 51

Teaching in student-centred active learning spaces: How rela...

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51st Annual Conference of the European Society for engineering Education, SEFI 2023

作者： Korpås, G.S. Andersen, T.H. Kahrs, M.S. Hansen, G. Røren, K.A.F. Department of Physics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Section for Teaching and Learning Support Education Quality Division Norwegian University of Science and Technology Trondheim Norway

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Automated Skin Lesion Analysis and Classification Using AI 4

Automated Skin Lesion Analysis and Classification Using AI

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Patra, Sudhansu Shekhar Gourisaria, Mahendra Kumar Singh, Satyendr Barik, Lalbihari School of Computer Applications Kiit Deemed to be University Bhubaneswar India School of Law Kiit Deemed to be University Bhubaneswar India School of Computer Engineering Kiit Deemed to be University Bhubaneswar India Bml Munjal University Computer Science & Engg Department Gurugram India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Rabigh21911 Saudi Arabia

ISBN: (纸本)9798331540364

Skin lesions are becoming a major global health concern, and survival rates can be greatly increased by employing dermoscopic pictures for early identification. It is a difficult undertaking to classify these skin lesions from dermoscopic images. The application of deep learning models has significantly improved this classification's accuracy. Because convolutional neural networks (CNNs) are better at extracting features, which improves classification results, they have been shown to be quite useful in this field. In light of this, the article attempts to create deep learning (DL) algorithms using artificial intelligence (AI) to differentiate between skin lesions as benign or malignant in dermoscopic pictures. The study presents OSSAE-BPNN, an automated method for the detection and classification of skin lesions. This method uses a backpropagation neural network (BPNN) for classification and an optimized stacked sparse autoencoder (OSSAE) for feature extraction. To determine the damaged lesion region, the multi-level thresholding segmentation methodology is part of the proposed OSSAE-BPNN method. The OSSAE performs the feature extraction, and the BPNN handles the categorization. Furthermore, the SGO (seagull optimization) technique is used to optimize the SSAE model's parameters. Extensive testing were conducted on a standard dataset to show the OSSAE-BPNN method's efficacy. The OSSAE-BPNN methodology fared better than other current methods across a range of evaluation parameters, according to the results. © 2024 IEEE.

关键词： Convolutional neural networks

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Assessing Quality of Service Performance in SDN Controllers: POX vs. OpenDaylight

Assessing Quality of Service Performance in SDN Controllers:...

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Advancements in Computing (ICAC), International Conference on

作者： Shashika Lokuliyana Pipuni Wijesiri Anuradha Jayakody Pradeep K.W. Abeygunawardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This research offers a comparative analysis of Qual-ity of Service (QoS) within Software Defined Networks (SDN) by assessing the POX and OpenDaylight controllers across various network topologies. The results show that OpenDaylight has a 32.59% higher average throughput than POX, demonstrating its superior data handling capabilities. Conversely, POX exhibits a 54.53% lower average delay and an 85.05% higher average jitter compared to OpenDaylight, highlighting its effectiveness in maintaining lower latency and variability in packet transmission times. These contrasting performances provide essential insights for network engineers in selecting the optimal SDN controller based on specific network demands, significantly influencing the overall network efficiency and stability.

关键词： Systematics Network topology Quality of service Jitter Throughput Real-time systems Stability analysis Delays Software defined networking Optimization

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer systems Science & engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer systems Science & engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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