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Image Captcha Blended with OTP for Secured Authentication 1

7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Bazeer Ahamed, B. Krishnamoorthy, Murugan University of Technology and Applied Sciences College of Computing and Information Sciences Al Musannah Oman School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

With the recent development in digital currency, the number of online transactions done every day is increasing exponentially. Along with the growing need for online transactions various attacks are developed to mimic an authenticated user, one of which is a phishing attack. The effort to access important materials such as credit card particulars, usernames, and passwords for mischievous reasons. We have proposed a unified model in which image CAPTCHA serves as the input for a hashing algorithm which serves as OTP generation, to avoid phishing attacks. This system is enhanced with more attack-handling mechanisms to intensify security in online application systems with better user authentication. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phishing

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Heartbeat Driven Network Health Assessment 64

Heartbeat Driven Network Health Assessment

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2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023

作者： Felzenbergs, Kristaps Bukauskas, Linas Majore, Ginta Sociotechnical Systems Engineering Institute Vidzeme University of Applied Sciences Valmiera Latvia Institute of Computer Science Vilnius University Vilnius Lithuania

ISBN: (纸本)9798350370294

We live in a fast-growing digital century where devices such as light bulbs, electrical cords, air refreshers, and other home appliances are becoming part of smart devices, leading to a fast-growing network with less trusted and less predictable computer network ecosystems than ever. This growth poses new threats that could not be easily identified using a common threat assessment and even with industry-leading anomaly-detecting machine learning solutions. Such solutions involve hard manual work on filtering out anomalies as network data patterns change similarly to human behavior. To get a trusted network health measurement, we propose a technique where we attempt to identify commonly known network errors or diseases. Further, we measure the network's heartbeat to see the current stress level, similar to a doctor's visit for a human. Finally, we show results from our experiments, giving an example of what the proposed methodology would output. © 2023 IEEE.

关键词： Domestic appliances

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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 IEEE.

关键词： Iterative methods

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Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet 2

Detection of Mango Leaves Disease using CSUBW based Residual...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Design and Development of Cloud based QR Coded Watermarking DLP system 19

Design and Development of Cloud based QR Coded Watermarking ...

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19th International Bhurban Conference on applied sciences and Technology, IBCAST 2022

作者： Ahmed, Syed Ahsan Ul Haq, Aftab Sheeraz, Muhammad Durad, Muhammad Hanif Pakistan Institute of Engineering and Applied Sciences Department of Computer and Information Sciences Islamabad Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Despite hectic security efforts in place today by large organizations, data breaches continue to occur, and identity theft is on the rise. Data leak prevention (DLP) is a suite of technologies aimed at restricting the loss of sensitive information that occurs in enterprises across the globe. By focusing on the location, classification, and monitoring of information at rest, in use and in motion, this solution can go far in helping an enterprise get a handle on what information it has, and in stopping the numerous leaks of information that occur each day. In this paper, first we suggest a policy for an enterprise to work on to prevent the data leakage and detection, depending upon access control and data location using QR code-based watermarking. QR code-based watermarking have been implemented using image and pdf files for detecting violations in accessed, downloaded, and uploaded files to central portal. © 2022 IEEE.

关键词： Classification (of information)

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A Comprehensive Machine Learning Based Ensemble Model for Prediction of Possible Diseases and Recommending Medicines 3

A Comprehensive Machine Learning Based Ensemble Model for Pr...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jangde, Priyanka Sethi, Kamal Kumar Singh, Narendra Pal Bhanodia, Praveen Mishra, Durgesh Computer Science and Engineering Acropolis Institute of Technology and Research Indore India Symbiosis University of Applied Sciences Indore India

ISBN: (纸本)9798350343274

Many instances remain untreated globally due to the lack and unavailability of medical services. In this regard, efficient monitoring and analysis of medical records will be helpful in timely identification and prediction of diseases. To enable a quicker and more precise diagnosis, a platform that keeps track of a patient's medical history and makes illness predictions based on the patient's present symptoms is needed. Early disease prediction allows users to determine the severity of a condition and take appropriate action timely. Thus, anticipation of illness in its early stage becomes essential but it's difficult for doctors to anticipate outcomes precisely and accurately based only on symptoms. Availability of medical data and its analysis with the help of machine learning techniques can help the healthcare system for appropriate predictions and recommendations of diseases. In this paper, based on the patient's symptoms and medical records, a general illness prediction system is proposed. Using the input image for disease prediction, we applied an ensemble model exploiting the KNN, SVM, Naïve bayes and deep learning techniques to accurately predict diseases. The proposed model superseded the existing techniques with 98% accuracy on experimental study performed using benchmark datasets. © 2023 IEEE.

关键词： Diseases

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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

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computer Modeling in Engineering & sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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Crafting the Future: Developing and Evaluating a Digital Mindset Competence Model for the Industrial Craft Sector 16

Crafting the Future: Developing and Evaluating a Digital Min...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Kocak, Seyma Pawlowski, Jan University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Applied Sciences Ruhr West Institute of Computer Science Bottrop Germany

ISBN: (纸本)9789897587160

The recent development of digitization has significantly influenced various sectors of the economy, and the Industrial Craft Sector is no exception. The transition to digital technologies and processes is inevitable and holds the potential for increasing efficiency and creating competitive advantages. This research used the Design Science methodology to develop a Digital Mindset Competence Model. This model comprises eight dimensions specifically tailored to the requirements and challenges of the Industrial Craft Sector. These dimensions aim to promote and strengthen the digital mindset among professionals in the Industrial Craft Sector. To ensure the validity and relevance of this model, experts from the Industrial Craft Sector were involved in a qualitative methodology. The combination of scientific methodology and practical experience ensures a comprehensive perspective and guarantees the applicability of the developed model. The results of this research underscore the importance of digital transformation in the Industrial Craft Sector and the necessity of a digital mindset. The developed Digital Mindset Competence Model provides a targeted approach to promoting digital competencies in the Industrial Craft Sector and guides future developments in this area. It becomes evident that an appropriate digital mindset is essential to optimally leverage the potentials of digitization in the Industrial Craft Sector and successfully navigate continuous change. This scholarly contribution contributes to raising awareness of the significance of a digital mindset in the Industrial Craft Sector. It forms a basis for further investigations and practical applications within digital transformation. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Design Science Research Digital Competence Digital Mindset Industrial Craft Sector

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Exploring the Boundaries of Digitalization of Cultural Heritage: Opportunities, Challenges and Future Directions

Exploring the Boundaries of Digitalization of Cultural Herit...

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2024 Lock-in or log out? Wie digitale Souveranitat gelingt, INFORMATIK 2024

作者： Reckers, Sophie Biwer, Simon University of Applied Sciences Bonn-Rhein-Sieg Department of Computer Science Institute of Visual Computing Grantham-Allee 20 Sankt Augustin53757 Germany

ISBN: (纸本)9783885797463

This contribution explores the opportunities and challenges of digitalizing cultural heritage, using the Digitalization of Cultural Heritage project as a case study. The project, a collaboration among universities from multiple countries, focuses on creating 3D models of historical artifacts, exemplified by the 3D modelling of Roman-period fragments using photogrammetry. The paper discusses the broader implications of digitalization with a particular focus on the use of AI technologies, including its potential to enhance education, accessibility, artifact preservation, and cultural tourism. It also addresses the technical and ethical challenges involved, emphasizing the need for ongoing innovation and interdisciplinary collaboration to maximize the benefits of digital cultural preservation. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： 3D Model Digitalization of Cultural Heritage Photogrammetry Virtual Preservation

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