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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Patil, Rajendra V. Aggarwal, Renu Shivaji Deore, Shailesh Sunrise University Department of Computer Engineering Rajasthan Alwar India SunRise University Department of Computer Science and Engineering Rajasthan Alwar India SSVPS BSD College of Engineering Department of Computer Engineering Dhule India

ISBN: (纸本)9798350395235

Image Segmentation is necessary stage in image investigation. In computer vision, image segmentation is the process of dividing digital image into multiple segments. Dividing the image into various parts simplifies the analysis of image. In numerous image analysis and computer visualization uses, including remote sensing, robot vision, medical image analysis, and pattern recognition, the subdivision of image into segments is an important preprocessing stage. Finding the outlines of objects in the images is the fundamental step in the segmentation of pictures. Zero crossover, Canny, Gradient dependent, zero cross, Laplacian are examples of traditional edge detection techniques. This study presents a novel method based on phase congruency for edge segmentation. As compared to traditional edge detection methods, advantage of phase congruency is its invariance to uneven illumination. To obtain edge segmentation outcomes, edge lines were obtained using phase congruency. Long edges give an approximate sense of the object's arrangement in the picture. Color likeness between edges is determined while short edges are removed. Color similar edges assigned same label. Performance analysis shows that proposed method produces good results for synthetic images and natural real world images in which objects are well separated by boundaries whereas it results in broken edges for images having complex structure. © 2024 IEEE.

关键词： Image segmentation

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A nano-scale arithmetic and logic unit using a reversible logic and quantum-dots

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Journal of Supercomputing 2024年第1期80卷 395-412页

作者： Navimipour, Nima Jafari Ahmadpour, Seyed-Sajad Yalcin, Senay Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Computer Engineering Nisantasi University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to reversible logic and quantum-dot cellular automata (QCA) because of their intrinsic capacity to decrease energy dissipation, which is a crucial need for low-power digital circuits. QCA will be the preferred technology for developing the subsequent generation of digital systems. These technologies played a substantial role in the design of the ALU for operations such as multiplication, subtraction, and division. In developing reversible logic and QCA technologies, the ALU is frequently studied as a central unit. Implementing an efficient ALU with low quantum cost and a small number of cells based on an efficient reversible block can solve all previous issues. Therefore, this research constructs a Feynman gate, a Fredkin gate, and full adder circuits using reversible logic and QCA technology. Using all of the specified circuits, a 20-operation ALU is constructed. The power consumption of the proposed ALU under various energy ranges demonstrated significant improvements over earlier designs. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Energy utilization

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Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

Journal of Energy Systems

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Journal of Energy Systems 2025年第1期9卷 132-158页

作者： Elbaş, Hakan Bilgin, Turgay Tugay Bursa Technical University Graduate School Department of Intelligent Systems Engineering Bursa Turkey Bursa Technical University Faculty of Engineering and Natural Sciences Department of Computer Engineering Bursa Turkey

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting. © 2025 Erol Kurt. All rights reserved.

关键词： Demand response

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A Predictive Model of the Stock Market Using the LSTM Algorithm with a Combination of Exponential Moving Average (EMA) and Relative Strength Index (RSI) Indicators

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Journal of The Institution of Engineers (India): Series B 2024年第5期105卷 1145-1157页

作者： Dhokane, Rahul Maruti Agarwal, Sohit Computer Science and Engineering Department Suresh Gyan Vihar University Rajasthan Jaipur India

This research paper has focused on the integration of promising stock market indicators such as the relative strength index (RSI) and different versions of the exponential moving average (EMA) (i.e., 50-day, 100-day, and 150-day EMA) with the long short-term memory (LSTM) machine learning algorithm for stock price prediction. LSTM is the most robust version of recurrent neural network because it can handle the "vanishing gradient" problem effectively and handle lengthy time series data. The selected dataset was taken from NSE India using Yahoo Finance. In our proposed research, we used RSI and EMA data along with basic features such as open, high, low, close, and volume (OHLCV) information. The selected technical indicators and OHLCV data act as input features for the LSTM model. The results show that the integration of technical indicators along with traditional OHLCV improves the performance of the stock closing price prediction model. The evaluation metrics used in this research are the RMSE, MAPE, and R2 score. The improvements in the R2 score and decreases in the RMSE and MAPE indicate that the proposed technique performs well in stock closing price prediction. The proposed technique helps to improve intraday trading by predicting the closing price of the stock for the next day and the trends of selected stocks in the near term. © The Institution of Engineers (India) 2024.

关键词： Long short-term memory

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Exploration, Collaboration, and Applications in Multi-Agent Reinforcement Learning

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Performance Evaluation Review 2025年第3期52卷 31-34页

作者： Chen, Jingdi Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

In recent years, the field of humancentric decision-making has emerged as a critical area of research, driven by its potential to fundamentally reshape how decisions are made across a variety of complex systems. Human-centric decision-making is characterized by considerable variability, which can be both advantageous and problematic. Variability is beneficial in contexts like brainstorming or problem-solving, where diverse perspectives lead to innovation. However, in situations where consistency is crucial- such as public health, safety, and fairness-this variability can have negative consequences. For instance, when radiologists interpret the same mammogram differently, it can result in unnecessary medical procedures. Similarly, in military operations, inconsistent judgments by sonar technicians regarding potential threats can jeopardize both crew safety and mission success. This highlights the critical need for a deeper understanding of decisions where the stakes are high. Thus, it is important to deepen our understanding of the decision-making process in completed and dynamic scenarios that involve multiple decisionmakers. This should guide the creation of validated methods to identify, measure, model, and adjust variability in judgments to improve decision-making in multi-agent environments. © 2025 Copyright is held by the owner/author(s).

关键词： Reinforcement learning

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A reliable and secure video steganography method based on 4d-logistic mapping

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Multimedia Tools and Applications 2025年 1-31页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj16278 Saudi Arabia

Data security is becoming more and more crucial due to developments in communication and information technology, particularly when it comes to video transmission. This research provides a unique approach that combines 4D Logistic Mapping (4DLM) with MASK cryptography technology to improve video data security. The five steps of the suggested approach are as follows: gathering video, extracting frames, compressing data using the LZW technique, encrypting it using MASK, and embedding it using 4DLM. To protect the video frames, MASK encryption uses a single matrix and four arrays, advancing through matrix initialization, key scheduling, and substitution/diffusion. 4DLM is used after encryption to improve the system's overall security and effectiveness. This model's major goal is to keep performance high while safeguarding video data from unwanted access. Data confidentiality is significantly improved by the suggested strategy, as shown by simulations performed with the Python program. For the example video sequences, the Structural Similarity Index (SSIM) was close to 99%, which indicates better performance than current methods. MASK and 4D Logistic Mapping together provide a powerful answer for today's data security issues by providing an effective and safe method of video data protection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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A Deep Learning-Based Approach for Damage Detection in Cultural Heritage Images and Generating Heat Maps 2

A Deep Learning-Based Approach for Damage Detection in Cultu...

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2nd International Conference on Sustaining Heritage, ICSH 2024

作者： Ogdu, Cagatay Umut Yilmazer, Merve Karakose, Mehmet Firat University Computer Engineering Department Elazig Turkey Munzur University Computer Engineering Department Tunceli Turkey

ISBN: (纸本)9798350355727

Cultural structures may be damaged and damaged over time due to external factors such as natural and social disasters, which may lead to the loss of national assets if cultural assets are not protected. Therefore, the protection of cultural heritage is of great importance not only for today's generations but also for future. In recent years, deep learning and image processing have made great progress and have become very effective for detecting and analyzing patterns. These techniques can also be used to solve complex problems such as damage detection of structures in the field of cultural heritage. Deep learning methods can be used to determine the current status of structures and detect possible damage by working on images obtained from different angles of structures, while also reducing human errors. In this study, as an important step for the protection of cultural heritage, a detection method was developed using deep learning models on damaged and intact cultural heritage building images. The data set used in the study consists of damaged and intact cultural heritage building images. An attempt was made to determine the model that gave the best results by using pre-trained models with the Transfer Learning method. Heat maps were created using the Grad-CAM method to identify damaged areas on the trained models, thus making it possible to more clearly identify the damaged areas in the image. This method can play an important role in preserving cultural heritage. It can be used to detect damaged areas and direct them to repair. © 2024 IEEE.

关键词： Deep reinforcement learning

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Resource scheduling in cloud environment using particle swarm search algorithm

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International Journal of Cloud Computing 2024年第4期13卷 330-352页

作者： Majhi, Malay Kumar Kabat, Manas Ranjan Sahoo, Satya Prakash Department of Computer Science and Engineering Veer Surendra Sai University of Technology Sambalpur India

Cloud computing has gained significant popularity as a platform for processing large-scale data analytics, offering benefits such as high availability, robustness, and cost-effectiveness. However, job scheduling in cloud systems presents a major challenge, as it directly impacts execution time and operational costs. To address these issues, this paper presents a novel multi-adaptive convergent particle swarm optimisation (MAC-PSO) algorithm designed to decrease the failure rate, minimise makespan values, and enhance resource utilisation. The round Robin scheduling method aids in task execution by determining the appropriate time-space allocation. The proposed algorithm’s performance is compared to that of the TLBO algorithm, demonstrating that MAC-PSO outperforms both TLBO and the original PSO. Moreover, a comprehensive analysis is proposed to evaluate the performance metrics within the MAC-PSO algorithm. Notably, MAC-PSO effectively increases the ratio of solutions that dominate previous algorithmic approaches and identifies a greater number of solutions that cater to user preferences. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud platforms

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Diabetic retinopathy detection by fundus images using fine tuned deep learning model

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Multimedia Tools and Applications 2024年第39期83卷 86657-86679页

作者： Singh, Satya Prakash Gupta, Pankaj Dung, Rakshita Department of Computer Science & amp Engineering Birla Institute of Technology Ranchi Mesra India

This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved utilizing three datasets: Messidor, IDRiD (Indian Diabetic Retinopathy Detection), and APTOS 2019 Blindness Detection, which collectively encompassed 5,379 fundus images. Different types of processed fundus images were fed into the model to determine the optimal pre-processing approach for stage detection in Diabetic Retinopathy. The model was assessed on the original dataset with some augmentation techniques applied. According to the training data, the model achieved a maximum accuracy of 72%. However, converting the dataset to grayscale yielded an improved accuracy of 80%. Similarly, extracting the green, red, and blue channels individually resulted in accuracies of 72%, 76%, and 73% respectively. Notably, when the green channel extracted images underwent histogram equalization, the model achieved its highest accuracy of 83%. Furthermore, the application of a Sobel filter to the red channel images led to a maximum accuracy of 51%. Finally, to determine the effectiveness of each processed image type, sensitivity and specificity measures were compared. Among all the variations, the green channel extracted images with histogram equalization demonstrated superior performance in correctly identifying the respective classes, outperforming the other approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural network models

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Quad Band Split Octagonal Ring Antenna with Integrated Stub for Satellite Communication-Dependent Wireless Applications

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Progress In Electromagnetics Research C 2024年 141卷 195-204页

作者： Suganthi, Jambulingam Kavitha, Thamizhchelvan Department of Electronics & Communication Engineering PES University Karnataka Bangalore560085 India Department of Computer Engineering New Horizon College of Engineering Karnataka Bangalore560103 India

The purpose of this study is to design a multiband antenna using metamaterial for efficient satellite communication. The majority of the antennae described in the available research suffer from a variety of limitations, including intricate designs, great footprints, and erratic radiation patterns. Therefore, there is a significant demand for antennae that are of a smaller size but nevertheless perform well. This paper proposes a quad-band stub-incorporated split octagonal ring antenna for satellite communication-dependent wireless applications. The suggested antenna is built on an FR4 substrate that measures 22 × 39 × 1.6 mm3. CST EM studio software is used for the entire simulation. The proposed antenna resonates at four different bands, with operating frequencies ranging from 2.15 GHz to 2.30 GHz, 2.86 GHz to 3.76 GHz (due to stub 1), 4.47 GHz to 5.24 GHz (due to stub 2), and 5.67 GHz to 6.35 GHz (due to stub 3). (due to gap between the stub). The proposed antenna has resonant frequencies of 2.23 GHz, 3.28 GHz, 4.77 GHz, and 5.89 GHz, and bandwidths of 153 MHz, 9011 MHz, 7692 MHz, and 6813 MHz. Parametric analysis is used to select the best values. The designed antenna is built and tested. The measured and simulated values for return loss, gain, E-plane, and H-plane are compared, and they agree. Its dual-band operation, compact size, steady radiation pattern, and gain above 1 dBi across the whole resonating band make it suited for ISM, WIFI, WLAN, WIMAX, 5G, and C band satellite applications. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Directional patterns (antenna)

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