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An Active Image Forgery Detection Approach Based on Edge Detection

computers, Materials & Continua 2023年第4期75卷 1603-1619页

作者： Hüseyin Bilal Macit Arif Koyun Department of IT Systems and Technologies Mehmet Akif Ersoy UniversityBucak Z.T.School of Applied Technology and BusinessBurdur15300Türkiye Department of Computer Engineering Suleyman Demirel UniversityEngineering FacultyIsparta32200Türkiye

Recently, digital images have become the most used data, thanks tohigh internet speed and high resolution, cheap and easily accessible digitalcameras. We generate, transmit and store millions of images every *** of these images are insignificant images containing only personal ***, in many fields such as banking, finance, public institutions,and educational institutions, the images of many valuable objects like IDcards, photographs, credit cards, and transaction receipts are stored andtransmitted to the digital environment. These images are very significantand must be secured. A valuable image can be maliciously modified by anattacker. The modification of an image is sometimes imperceptible even by theperson who stored the image. In this paper, an active image forgery detectionmethod that encodes and decodes image edge information is proposed. Theproposed method is implemented by designing an interface and applied on atest image which is frequently used in the literature. Various tampering attacksare simulated to test the fidelity of the method. The method not only notifieswhether the image is forged or not but also marks the tampered region ofthe image. Also, the proposed method successfully detected tampered regionsafter geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEGcompression.

关键词： Image forgery image tampering edge detection

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Backup subscription scheme for differential protection using IEC61850-9-2 sampled values

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Energy Informatics 2024年第1期7卷 110页

作者： Katoulaei, Mohammad Khalili Rahmani, Aamir Høidalen, Hans Kristian Oleinikova, Irina Mork, Bruce Department of energy systems Norwegian University of Science and Technology Trondheim Norway Department of electrical power system Michigan Technical University Michigan United States Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI49931 United States

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. © The Author(s) 2024.

关键词： Hardware-in-the-loop simulation

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Deep Learning Technique for Oropharyngeal Cancer Detection in Medical Imaging 7

Deep Learning Technique for Oropharyngeal Cancer Detection i...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kumar, R. Pazhanirajan, S. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chidambaram India

ISBN: (纸本)9798350340600

Oropharyngeal carcinoma is a life-threatening disease that requires prompt diagnosis and treatment. This study uses deep learning algorithms to present a novel method for improving medical imaging to detect oropharyngeal cancer. Deep learning has demonstrated immense potential for medical image processing applications in recent years. This research capitalizes on this opportunity by devising a dependable model for detecting oropharyngeal cancer. Examining oropharyngeal cancer with an emphasis on the critical significance of early detection. This study emphasizes the need for more precise and efficient diagnostic techniques. This work presents a comprehensive strategy from data acquisition and cleansing to the development of the model's underlying architecture, training, validation, and explication. The results section presents quantitative and qualitative data demonstrating the model's ability to detect oropharyngeal cancer. The proposed model has the potential to improve the quickness and accuracy with which oropharyngeal cancer is diagnosed, thereby benefiting patients. It emphasizes the need for additional research and the implementation of deep learning innovations in healthcare. © 2023 IEEE.

关键词： Medical imaging

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and Technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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Leveraging power consumption for anomaly detection on IoT devices in smart homes

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14045-14056页

作者： Nimmy, K. Dilraj, M. Sankaran, Sriram Achuthan, Krishnashree Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India

Anomaly detection in smart homes is paramount in the prevailing information age as smart devices remain susceptible to sophisticated cyber-attacks. Hackers exploit vulnerabilities such as weak passwords and insecure, unencrypted data transfer to launch Distributed Denial of Service (DDoS) attacks. Sensible deployment of conventional security measures is jeopardized by the heterogeneity and resource constraints of smart devices. This article presents a novel approach that leverages the power consumption of Internet of Things (IoT) devices to detect anomalous behavior in smart home environments. We prototype a smart camera using Raspberry Pi and gather power traces for normal activity. Furthermore, we model DDoS attacks on the experimental setup and generate attack traces of power consumption. Besides, we compare the performances of several machine learning models for accurate prediction of the presence of anomalies. A deep feed-forward neural network model achieves an accuracy of 99.2% compared to other models. Empirical evaluations of the proposed concept affirm that power consumption is a promising parameter in detecting anomalies in smart homes. The proposed method is suitable for smart homes as it does not impose additional overhead on resource-constrained IoT devices. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Identifying player skill of dota 2 using machine learning pipeline

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Discover Artificial Intelligence 2024年第1期4卷 41页

作者： Pengmatchaya, Methasit Natwichai, Juggapong Data Science Consortium Chiang Mai University Huay Kaew Muang Chiang Mai50200 Thailand Department of Computer Engineering Faculty of Engineering Chiang Mai University Huay Kaew Muang Chiang Mai50200 Thailand

The esports industry is one of the prominent business sectors in the digital era, particularly, Multiplayer Online Battle Arena (MOBA) games which gain much attention from gamers and streaming audiences. Among such games, Defense of the Ancient 2 or Dota 2 is the record holder for the highest prize esports tournament. Therefore, various companies and investors start their esports teams to compete in the Dota 2 tournaments, the Internationals. To success in the competition, player recruitment is a crucial process as it usually takes considerable effort to find a skillful player. Watching the game replay to evaluate the player’s skill is one of the approaches. However, it can be too exhaustive, also some player’s ranking, which represent the player’s skill, are often not available. In this paper, we propose an effective machine learning pipeline to evaluate the player’s skill. Our designed pipeline includes data collection, feature engineering, and machine learning modeling. We show the data collection process using open-source API. An effective method for feature engineering is proposed. Features, e.g., end-game, or tactical decision related statistics, are incorporated along with the resource in the game distribution, harassment tactic, or spatiotemporal features, in order to provide effective models. Subsequently, we apply major machine learning models based on a single game data, i.e., logistic regression and random forest, to the processed data. The most effective model can achieve up to 0.7091 precision, 0.5850 recall, 0.6411 F1-score, and 0.8123 ROC AUC score. © The Author(s) 2024.

关键词： Pipelines

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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Optimal parameter estimation of three solar cell models using modified spotted hyena optimization

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 361-372页

作者： Gafar, Mona El-Sehiemy, Ragab A. Hasanien, Hany M. Abaza, Amlak Department of Computer Science College of Science and Humanities in Al-Sulail Prince Sattam Bin Abdulaziz University Kharj Saudi Arabia Machine Learning and Information Retrieval Department Artificial Intelligence Kafrelsheikh University Kafr El Sheikh Egypt Electrical Engineering Department Faculty of Engineering Kafrelsheikh University Kafr El Sheikh Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo11517 Egypt

This paper is concerned with identifying the optimal parameters of solar cell by using a modified spotted hyena optimization algorithm (MSHOA). In the MSHOA, the optimization process initializes random search agents then selects the best agents. The MSHOA modifies the original spotted hyena optimization algorithm by using an accelerating function, which improves the performance of reaching the optimal solution. The convergence occurs rapidly and realizes the global optimization with small iterations number. The numerical results obtained with different models emphasize that the MSHOA has the ability and stability to estimate the global optimal decision variables of solar cell module with a minimal root mean square error compared with other algorithms existed in literature. Moreover, three electrical models called single diode model, double diode model and triple diode model TDM are accurate for depicting electrical behavior of PV modules. It is truth to say that TDM is the most accurate model among the three models. The significant agreements between the estimated model using MSHOA, at different operating temperatures and irradiances, and the measured data assert on the effectiveness of the proposed algorithm and the accuracy of PV model. All results emphasize that the MSHOA is an effective and reliable optimization algorithm for finding the optimal parameters of solar cell modules. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Solar panels

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A distributed B+Tree indexing method for processing range queries over streaming data

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Cluster Computing 2024年第2期27卷 1251-1274页

作者： Safaee, Shahab Mirabi, Meghdad Rahmani, Amir Masoud Safaei, Ali Asghar Department of Computer Engineering Faculty of Engineering South Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Medical Informatics Faculty of Medical Sciences Tarbiat Modares University Tehran Iran

A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries submitted by the user when compared with the traditional databases. In this paper, we propose a system architecture which includes components for both distributed indexing of streaming data and distributed processing of range queries on streaming data. Instead of creating a large and centralized B+Tree index structure, we create a set of small B+Tree indexes in such a way that a B+Tree index can be created for every partition of streaming data. We also design a distributed range search algorithm which can be used by each individual machine inside a Spark cluster to independently process range queries on each partition of streaming data. By exploiting the proposed system architecture, the process of indexing of streaming data and the process of querying over streaming data can be performed in a distributed and parallel manner. By performing several experiments, we demonstrate that our proposed indexing method is scalable and efficient for processing range queries on streaming data compared to the existing centralized B+Tree indexing methods and therefore, it can be used for applications involving data streams with a large volume of data elements and a large number of range queries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Query processing

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Dynamic Trust Management and AI-Based Multi-Factor Authentication for 6G Machine Type Communications 1

Dynamic Trust Management and AI-Based Multi-Factor Authentic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sinha, Garima Agarwal, Ankita Faculty of Engineering and Technology Department of Computer Science Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

New security concerns about the transmission of sensitive data over enormous networks of linked devices have arisen with the advent of the 6G era and the broad adoption of massive machine-type communication (MTC). The authors of this paper provide a fresh strategy for MFA and dynamic trust management in 6G networks that make use of AI to solve these issues and improve MTC security. This study presents a novel approach to behavioural profiling that takes into account the strengths and weaknesses of machine-type devices. It was necessary to create simulated data to represent many situations due to the wide range of possible network circumstances, device characteristics, ambient variables, applications load of work, and security needs. The efficacy of the suggested remedy was evaluated during six simulation cycles. The findings showed that the framework can adapt to changing network circumstances and device behaviours, with stability demonstrated across many situations. The accuracy rates for implementing MFA ranged from 0.49 to 0.57. This MFA and AI-driven dynamic trust management framework has great potential to improve 6G network MTC security;its adaptability and resilience provide a solid basis for meeting the security concerns that will inevitably arise in the future of 6G networks. To maximize performance and assure successful deployment, more modifications and real-life testing are suggested. © 2025 IEEE.

关键词： Authentication Protocol

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