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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第1期86卷 3-18页

作者： Mutasher Rashed, Baidaa Popescu, Nirvana Computer Science Dept. National University of Science and Technology POLITEHNICA of Bucharest Romania Computer Science Dept. University POLITEHNICA of Bucharest Romania

Medical imaging coupled with Artificial Intelligence (AI) applications, in particular Deep learning (DL) and Machine Learning (ML), can speed up the disease diagnostic process. The purpose of this work is to present a novel disease detection system by suggesting a new Convolutional Neural Network (CNN) model and combining the CNN features with three of ML classifiers and suggesting a new classifier using the stacking model. The proposed system was used in binary and multi-classification and applied to two different medical datasets. The proposed model was evaluated using accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC, achieving robust results. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Convolutional neural networks

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ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification

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Informatica (Slovenia) 2024年第7期48卷 103-112页

作者： Sabir, Aliea Ali, Huda A. Aljabery, Maalim A. Faculty of Computer Science and Information Technology Computer Information System Dept. University of Basrah Basrah Iraq Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah Iraq

Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as a natural language processing (NLP) to understand and express emotions. In this study we analyzed the sentiment of ChatGPT labeled tweet datasets sourced from the Kaggle community using five Machine Learning (ML) algorithms;decision tree, KNN, Naïve Bayes, Logistic Regression, and SVM. We applied three feature extraction techniques: positive/negative frequency, a bag of words (count vector), and TF IDF. For each classification algorithm. The results were assessed using accuracy measures. Our experiments achieved an accuracy of 96.41% with the SVM classifier when using TF- IDF as a feature extraction technique. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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AES-Based Steganography Using Blockchain: A Novel Approach for Secure Text Hiding in Encrypted Images

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Informatica (Slovenia) 2024年第21期48卷 67-78页

作者： Salim, Batool Arif Aljabery, Maalim A. Younis, Hameed Abdulkareem Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah Iraq

Steganography is a technique used to hide data within other data, emerging from the realization that information is valuable and must be concealed. By considering the potential of blockchain technology, which produces and stores data in an immutable chain, it is clear that steganography can be effectively applied alongside blockchain to hide information. This approach eliminates the need for traditional hiding methods. In this study, we aim to hide text messages within encrypted images using a new steganography-based blockchain, making them appear as ordinary encrypted images. The AES algorithm in CBC mode was used to encrypt both images and texts. Each image was split into 32-byte blocks, with a special block allocated for text, allowing for a text size of 32 characters. The robustness of the proposed technique against differential attacks was assessed using unified averaged changed intensity (UACI), number of pixels change rate (NPCR), entropy analysis, and correlation analysis. The outcomes are 99.6221% for NPCR, 33.5886 for UACI, and 7.9992 for the entropy value. Both statistical measures and differential metrics confirm the algorithm's effectiveness. This shows that the proposed encryption method generates random images and secure texts that are resistant to differential attacks and offer a prominent level of security. © 2024 Slovene Society Informatika. All rights reserved.

关键词： AES algorithm blockchain CBC mode correlation analysis entropy analysis NPCR secure texts steganography UACI

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Time series forecasting using transformer neural network

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International Journal of computers and Applications 2024年第10期46卷 880-888页

作者： Bhogade, Vaibhav Nithya, B. Dept. of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is *** model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Prediction models

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Smart Traffic Monitoring through Drone Images via Yolov5 and Kalman Filter 5

Smart Traffic Monitoring through Drone Images via Yolov5 and...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Hanzla, Muhammad Ali, Shuja Jalal, Ahmad Air University Dept. of Computer Sciences Islamabad Pakistan Air University Dept. of Creative Technology Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Monitoring traffic is of vital relevance in our modern scenario. Over the years, standard ways of capturing data, such as induction loops and camcorders, were applied for this aim. Nevertheless, the introduction of unmanned aerial vehicles (UAVs) has opened new possibilities in this subject, leading to extensive study in computer vision. However, there are still issues in object detection and tracking because of the complexities that come from a high number of objects, shifting heights of unmanned aerial vehicles (UAVs), and variable lighting conditions. Our study provides a new approach that combines centroid tracking with the YOLOv5 algorithm to successfully monitor and identify vehicles. To acquire an accurate analysis, we began our method by aligning and referencing aerial images. These basic measurements assisted the advancement to next phases covering feature extraction, segmentation of the region of interest, and tasks connected to detection and tracking. The effectiveness of our technique was tested by applying it to the VAID dataset, resulting in amazing levels of accuracy demonstrated by our recommended solution. Our system exhibited an unparalleled accuracy of 98.5% for object detection and a reasonable 90.9% for tracking tests. The validation indicates the robustness and efficacy of our designed system in solving the obstacles given by sophisticated aerial surveillance conditions. © 2024 IEEE.

关键词： Object recognition

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Automated Gait Event Detection in Sports: A Novel Approach Using Ant Colony and XGBoost

Automated Gait Event Detection in Sports: A Novel Approach U...

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2024 International Conference on Frontiers of Information technology, FIT 2024

作者： Wahid, Wasim Hanzla, Muhammad Rahman, Hameed Ur Jalal, Ahmad Dept. of Creative Technology Air University Islamabad Pakistan Dept. of Computer Science Air University Islamabad Pakistan Dept. of Computer Gaming Development Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

Advanced techniques for body part detection and marker-less sensor-based cue selection is needed for automated human posture estimation (A-HPE) systems to efficiently identify complicated activity movements. The complicated motions during sports and fitness activities and the differences in lighting conditions make it difficult to identify human activities using vision sensors. This study presents an innovative approach to automatically recognize distinct gait events (GEDs) in sporting activities. Our system integrates techniques for detecting individuals and extracting crucial body points to establish a skeletal framework. Subsequently, we employ analyses on these key points and body features, using methods such as histograms of oriented gradients (HOG) and joint angle computations. To address the variability of sports movements, we utilize two robust algorithms: ant colony optimization and XGBoost. These algorithms facilitate the classification of movements and the assignment of labels to each event. Evaluation on a publicly available dataset of Olympic sports demonstrates that our GED system achieves an accuracy of 88.81%, surpassing existing methodologies. Application for the suggested approach in man-machine interactions include augmented reality, service bots, e-health fitness, and surveillance of security. © 2024 IEEE.

关键词： Ant colony optimization

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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IoT IN EDUCATION - PHOTOVOLTAIC PANEL SYSTEMS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第1期86卷 329-342页

作者： Oprea, Marin Mocanu, Mariana The National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Computer Science The National University of Science and Technology POLITEHNICA Bucharest Romania

In this paper we presented a synthesis of the design, construction and implementation activities, at the school level, of an intelligent system for transforming solar energy into electricity based on photovoltaic panels, with the possibility of real-time monitoring, via Internet, of its functional parameters: voltage (U), current (I), power (P) and electrical energy (W) produced. Since the purpose of the project was educational, we started the activities with the design and realization of a didactic solar panel, composed of polycrystalline solar cells. To determine its functional parameters, we used an experimental method different from the classical method (based on measurements made with multimeters). This new method was based on the INA219 current-voltage sensor connected to an Arduino Uno board and allowed real-time representation of the I-U and P-U characteristics of the panel using the Excel add-in Data Streamer. The technical parameters of the panel were studied in different lighting conditions (artificial light and natural light). After the realization and functional testing of this didactic panel, we included in the scope of the experimental activities some solar panels of commercial origin, of low and medium power. These activities prepared the framework for the realization of an IoT monitored photovoltaic system. The functional parameters of the panel were monitored through the open source IoT platform-ThingSpeak. The remote control of the consumers was realized both via Bluetooth and WiFi connection. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Solar panels

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Multi-sensors Fused IoT-based Home Surveillance via Bag of Visual and Motion Features 3

Multi-sensors Fused IoT-based Home Surveillance via Bag of V...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Azmat, Usman Jalal, Ahmad Javeed, Madiha Air University Dept. of Creative Technology Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

Internet of things (IoT) represent the small devices connected together wirelessly collecting data to make lifestyle convenient. Inertial measurement units (IMU) and cameras connected together to collect data from multiple indoor activities can also support home surveillance systems. The traditional closed-circuit television is out-fashioned due to the huge volume of storage requirements and not connected together to notify users immediately of apprehensive activities. Therefore, this paper proposes an IoT-based surveillance system for indoor environments that will upkeep the security methods inside the home. For this purpose, the fused multisensors-based data is acquired from two state-of-the-art datasets, namely, opportunity++ and CMU-MMAC. This acquired data from IoT devices is further pre-processed through multiple filtering techniques according to the type of data. Then, a skeleton model has been designed for the filtered video frame sequences. Furthermore, a bag of visual and motion features has been extracted using three different techniques followed by their discrimination. Finally, the IoT-based surveillance system detects indoor activities and provides feedback to the user. © 2023 IEEE.

关键词： Internet of things

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