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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

Remote Sensing in Earth Systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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Gastric Cancer Unveiled: Navigating the Terrain of Diagnosis and Therapy 7

Gastric Cancer Unveiled: Navigating the Terrain of Diagnosis...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Aroop, D.S. Sujitha Juliet, D. Ebenezer, Shamila Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372816

Gastric cancer (GC), commonly referred to as stomach cancer, is a perilous and potentially life-threatening form of cancer that impacts the digestive system. It significantly raises the risk of cancer related mortality. The timely identification and diagnosis of GC can significantly enhance the prospects of effective treatment and increased survival rates. As a result, it is important to know the causes and symptoms of stomach cancer and to get medical attention if anything changes. Detecting GC at an early stage is crucial for both successful treatment and improved long-term outlook for patients. As a result, developing effective approaches for early identification of this condition is critical. In this regard, new technologies for the accurate and reliable detection of GC have been created. These methods are capable of identifying alterations in the tissue composition of the stomach that suggest the presence of malignancy. They can also detect biomarkers linked to the disease, allowing for earlier identification. Using these tools, clinicians can diagnose GC in its early stages, resulting in improved outcomes for patients. This paper discusses some of the specific approaches used to detect stomach cancer. © 2024 IEEE.

关键词： Lung cancer

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Safeguarding Music Artists Through Machine Learning Based Singer Classification 17

Safeguarding Music Artists Through Machine Learning Based Si...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Maisha, Jannatim Mahtab, Muhammad Azmain Hossain Swarnali, Farhana Tabassum, Tamanna Rouf Shawon, Md. Tanvir Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350387674

Across various fields, the area of Bengali music, with its rich cultural heritage, is highly unexplored but identifying and classifying singers has become vital for safeguarding intellectual property and encouraging the music industry. Precise identification of Bengali singers is crucial for preserving singers' rights and ensuring fair compensation. This paper shows the effectiveness of different Convolutional Recurrent Neural Network (CRNN) and Recurrent Neural Network (RNN) based architectures on singer classification, specifically in Bengali songs, leveraging Mel-spectrogram features. In our study, the superiority of CRNN models over RNN and traditional ML models has been showcased, and among ML models our ensemble technique outperformed the rest. Our CRNN model achieved an F1-score of 0.97, significantly outperforming traditional ML methods (F1-scores for Logistic Regression, Random Forest, SVM, KNN, and Ensemble are 0.76, 0.74, 0.72, 0.68 and 0.83 respectively). The significance of CRNN in singer classification for Bengali music has been highlighted in our study, which offers a solution to protect singers' rights by reducing copyright infringement. Additionally, our work has the potential to assist in the development of music recommendation systems. © 2024 IEEE.

关键词： Convolutional neural networks

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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A Deep Learning-Based Bengali Visual Question Answering System Using Contrastive Loss 6

A Deep Learning-Based Bengali Visual Question Answering Syst...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Zaman, Md. Tareq Zaman, Md. Yousuf Shah, Faisal Muhammad Mahi, Eusha Ahmed Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Visual Question Answering (VQA) is an interdis-ciplinary research area that uses image recognition, natural language processing, and cognitive understanding to interpret visual data and answer appropriate queries. This study aims to advance Bengali visual question answering (VQA) by proposing a contrastive loss approach to improve bengali VQA models contextual understanding between images and questions. We introduce the new VQA Bengali 1.0 dataset containing 1,864 social media images with 3,728 corresponding yes/no questions, which are splitted into train/test/val sets. Our approach involves extracting image features using ResNet50 and textual features with BangIa BERT variants. These features are fused through an attention mechanism. We incorporate contrastive loss during training to increase discrimination between positive and negative image-text pairs. Comparing combinations of ResNet50 with Sagor Sarker BangIa BERT and Sahaj BangIa BERT models, experiments demonstrate contrastive loss boosts accuracy over cross-entropy loss. The best model with ResNet50, Sahaj BangIa BERT and contrastive loss achieves 93.21 % accuracy. This work advances Bengali VQA through proposing an effective contrastive loss methodology and releasing a balanced dataset. By enhancing model contextual understanding, it has significant potential for further research innovations. © 2024 IEEE.

关键词： Image recognition

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Resource-aware routing in opportunistic networks: existing protocols and open research issues

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International Journal of Cloud Computing 2023年第2-4期12卷 367-398页

作者： Rani Malik, Amita Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat131039 India

Opportunistic networks provide an effective communication in challenging scenarios such as interplanetary communication, post-disaster situation, etc. As the nodes have limited resources, thus designing an efficient routing protocol in opportunistic networks always remains a challenging task. Several review papers presenting features and applications of opportunistic networks, mobility models, challenges, taxonomies of routing, and incentive schemes exist, but a comprehensive review on resource-aware routing appears to be missing. Therefore, we propose a novel taxonomy for categorising existing resource-aware routing protocols proposed in this field. These are mainly categorised into energy-aware, buffer aware, bandwidth aware, and hybrid routing that considers the energy of nodes, buffer space, bandwidth, and multiple parameters respectively to make forwarding or routing decision. Further, we discuss various existing routing protocols with their strengths and weaknesses, leading to open research issues. This survey aims to help future researchers devise a novel routing protocol for opportunistic networks. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Routing protocols

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Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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Smart Surveillance: Real-Time Object Detection and Recognition with Interactive Polygon Region Using Deep Learning

Smart Surveillance: Real-Time Object Detection and Recogniti...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Krishnan, Unni Kalaimani, Thyagarajan Sethuraman, R. Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The Traditional Surveillance system lacks the capability to focus on particular region at a time during the surveillance. This cause important detail in that region to be missed out and go unnoticed. This is a major issue as it causes security concern. Detection of any objects in that region should be done in real-time. The proposed system allows the user to create a polygon region in area of interest in the video and by using the capability of YOLOv8, which stands for You Only Look Once Version 8 this system can detect the object and classify them and then converting to csv file. By allowing the users to create the polygon region it gives the surveillance camera capability of highly precise monitoring. This system helps to monitor the regions that changes dynamically. Changing Traditional surveillance system as a whole can be expensive and time consuming. Our system helps to bring a cost-effective solution without the need of whole changing whole system. It provides extra security to current infrastructure without the need for a complete overhaul, making it a good choice for various security setups. © 2024 IEEE.

关键词： Object recognition

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An Integrated Secure Rail System For Accident Prevention and Anti-Hijacking Measures

An Integrated Secure Rail System For Accident Prevention and...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Pradeep, Ganesula Kiran, Samineni Uday Sathyadurga, V. Hindustan Institute Of Technology & Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

India has one of the world's longest rail systems, covering an amazing distance. However, Indian railways fall short of international norms in terms of dependability and passenger safety. In order to solve this, an improved interface solution this paper has been started to improve railway safety and prevent collisions. This method uses Arduino microcontrollers in conjunction with fingerprint sensor modules. In order to maintain safe and controlled train operations, trains fitted with these specialised fingerprint devices won't start their engines until after authenticating the fingerprints of authorised users. And also the system recognises when two trains are about to collide on the same track when fingerprint modules are activated simultaneously. The GSM module quickly sends the latitude and longitude coordinates to a specified web site after detecting something. The position and seriousness of the collision risk may be quickly determined thanks to this data interchange, which is essential for further processing and analysis. In addition, the apparatus features an LCD screen that offers instantaneous visual notifications to users on potential collision scenarios. By enabling proactive steps for collision prevention and easy connection with external monitoring systems, this novel solution strengthens overall railway safety rules. It makes use of web-based connectivity and cutting-edge sensor technology to do this. © 2024 IEEE.

关键词： Liquid crystal displays

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