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Low-resolution Digital Pre-compensation for Photonic Terahertz Communications

Journal of Lightwave Technology 2025年第10期43卷 4669-4678页

作者： Ran, Wanling Zhang, Lu Bai, Liga Wang, Zhanhong Wang, Can Zhang, Qun Yu, Xianbin Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Shandong Zhike Intelligence Computing Co. Ltd. Jinan250101 China

This study explores the application of noise shaping (NS)-based low-resolution digital pre-compensation technique for enhancing photonic terahertz communication systems. Through simulations and experiments, we examined the principles of an NS quantizer with error feedback and integrated this NS approach into the digital signal processing workflow at the transmitter. This scheme involves digital preprocessing to suppress quantization noise, which improves communication signal fidelity. By optimizing NS parameters via numerical simulations, we achieved a significant increase in the signal-to-quantization noise ratio (SQNR) by over 9 dB with a 3-bit quantizer. We then experimentally validated the NS technique in a photonic terahertz communication system to address quantization noise issues inherent in low-resolution digital-to-analog converters (DACs), demonstrating improved transmission performance. For an 8 Gbaud PAM4 signal generated by a 3-bit DAC, the NS technique enhanced the signal-to-noise ratio (SNR) by more than 2 dB and reduced the bit error rate (BER) by over half an order of magnitude. © 2025 IEEE.

关键词： Signal to noise ratio

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Safe Spaces Analysis for Mental Health 12th

Safe Spaces Analysis for Mental Health

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Kelawala, Harsh Shah, Zeal Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 India

ISBN: (纸本)9789819789450

Mental health is a paramount concern in contemporary urban environments, necessitating comprehensive approaches to understanding its determinants and formulating effective interventions. This research project adopts a multifaceted methodology to explore the factors influencing mental well-being in urban areas, specifically focusing on cities within Gujarat. By integrating Geographic Information Systems (GIS) technology and predictive modeling techniques, we analyze various socio-environmental factors spanning air quality, noise pollution, green space availability, educational attainment, population density, income levels, and employability. At the same time, the GIS approach encompasses all major cities across India, the Random Forest algorithm is tailored specifically to the cities of Gujarat, reflecting the unique socio-geographical context of the region. Through rigorous data analysis and visualization, we identify spatial patterns and correlations, enabling the identification of areas most vulnerable to mental health challenges and prioritizing targeted interventions. This work presents our research's methodology, findings, and implications, offering valuable insights for policymakers and stakeholders striving to cultivate safe and supportive urban environments conducive to optimal mental health outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Where Do We Go From Here? Strategies for Expanding Access to Undergraduate engineering in a Post-SFFA Environment

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Advances in engineering Education 2025年第3期12卷 7-15页

作者： Berhane, Bruk Slay, Kelly E. Smith, Paige E. School of Universal Computing Construction and Engineering Education STEM Transformation Institute Florida International University United States Peabody College Vanderbilt University United States Women in Engineering University of Maryland United States

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Dynamic Pricing Models in E-Commerce: Exploring Machine Learning Techniques to Balance Profitability and Customer Satisfaction

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IEEE Access 2025年 13卷 72994-73002页

作者： Guo, Xiaochen Zhang, Lei Management School of Anhui Business and Technology College Hefei230041 China Anhui Business and Technology College School of Information Engineering Hefei230041 China University of Technology MARA College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia

This research investigates the application of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, paired with gradient-based optimization techniques for dynamic pricing in e-commerce. The primary objective is to develop a pricing model that effectively balances profitability with customer satisfaction by leveraging sequential data, such as time-series and customer behavior patterns. The approach utilizes LSTM’s ability to capture long-term dependencies in sequential data, while optimization methods like Stochastic Gradient Descent (SGD) enhance model convergence and performance. Key findings include the superior predictive accuracy of LSTM-based models over traditional approaches like Linear Regression and Decision Trees, particularly in real-time data updates and price elasticity scenarios. Additionally, the analysis revealed that LSTM models could efficiently adapt pricing strategies in response to market dynamics, significantly improving profitability while maintaining customer satisfaction. This study provides valuable insights into the application of advanced machine learning techniques in e-commerce pricing. The results suggest that LSTM-based dynamic pricing models could optimize revenue generation, offering substantial implications for pricing strategy development in modern retail environments. Future work may explore hybrid models and multi-objective optimization techniques to further refine these models. © 2013 IEEE.

关键词： User experience

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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

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Computers, Materials & Continua 2025年第2期82卷 3005-3018页

作者： Yahia Said Yahya Alassaf Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Civil Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafha91911Saudi Arabia College of Science Northern Border UniversityArar91431Saudi Arabia

Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.

关键词： Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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AI Modeling and Water Quality Sensing Technique Proffers Water Security: An Open Review 3

AI Modeling and Water Quality Sensing Technique Proffers Wat...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Haidar, Sk Wasim Sutherlin Subitha, G. Sarmila, J. University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari District India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Chunkankadai India

ISBN: (纸本)9798331509675

Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water quality parameters and components. Artificial intelligence (AI) based WQSs and machine learning (ML) algorithms primarily enable long term operations for real time detention of water quality information at different time periods. Messaging services, alarms, alerts and other proper warnings are given to water quality managers or assessing communities while the quality deterioration occurs at the particular water quality checking sites. A good number of impressive challenges are identified in hosting the variety of sensors to manage, warehouse and analyze incessant water quality information that they generate periodically. This paper places interests in highlighting lack of suitable assessment standards and determine logistical deliberations for fruitful implementations together with transportation, storage services and appropriate trustworthy communication. The main objective is to identify the designs targeting water security that support to monitor, reassure and protect the water resources all the way through uninterrupted quality supervision in addition to AI features. This open review also discusses a variety of applications in water quality sensors, AI modeling, electronic tongues and indices, hence results the knowledge of improvement required for upgrading the quality in water resources through computer technologies and models. © 2025 IEEE.

关键词： Electronic tongues

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Generative Adversarial Network Based Advanced Framework for High Resolution Multi-Style Cartoon Image Synthesis in Animation and Game Development 1

Generative Adversarial Network Based Advanced Framework for ...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kathiravan, M. Ramesh, S. Buvanesvari, R. Sreesubha, S. Irumporai, A. Venkataramana, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of CSE Chennai India School of Computing College of Engineering and Technology Department of Computing Technologies Chennai India Rajalakshmi Engineering College Department of CSE Chennai India ICT Trainer Datec India QIS College of Engineering & Technology Department of CSE Andhra Pradesh Ongole India

ISBN: (纸本)9798350355611

Industries like entertainment, education, and advertisements require cartoon image generation as a most powerful business tool now a days. Classic systems such as texture synthesis and edge detection were largely unable to generate more natural images with an arbitrary artistic style. This research overcomes these limitations by presenting a Generative Adversarial Network (GAN)-based framework for high-resolution, style transferable cartoon picture generation. The use of GAN stems from the generator's creation of images and the discriminator's perception of which images it created, both of which aim to minimize and maximize costs, thereby increasing the diversity of outputs over time. The model was first trained with 40,000 images that were classified according to the anime-GAN dataset and then tested on 10,000 different anime images. Proposed a novel loss function that consists of both adversarial and content loss, allowing the generated images to preserve their structural relationships while providing better quality and style variety. Evaluation on Fréchet Inception Distance (FID) revealed that our model outperforms existing methods with a large margin both in image quality and adaptive diversity. It is fast, efficient, and versatile;it can generate cartoon images in a wide range of styles with no need for retraining. The proposed approach has numerous implications in animation and game development as a tool to generate cartoon images of high diversity and quality quickly, alleviating some limitations of existing traditional methods and past machine learning models. © 2025 IEEE.

关键词： Generative adversarial networks

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