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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ye, Xinchen Zhang, Aokai Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350368741

Guided Depth Super-Resolution (GDSR) enhances low-resolution (LR) depth maps by leveraging high-resolution (HR) color images. The primary challenges involve achieving effective cross-modal data alignment and fusion, as well as incorporating multi-scale information within the Transformer architecture. To address these challenges, we propose a novel network architecture named DRMPNet which integrates two key components: Offset-based Detail Refinement (ODR) and Structure-guided Multi-scale Perception (SMP). ODR leverages offset calibration and window cross-attention to align and fuse LR depth maps with color images, effectively recovering local depth details. Meanwhile, SMP employs a structure generator and multi-scale cross-attention to capture scene details and structures at multiple scales, thereby enhancing the network's contextual understanding. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： Depth Map Guided Super-Resolution Transformer

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Deep Learning Empowered Natural Language Understanding Voicebot's for farmers: Kissan Samvaad Initiative 8

Deep Learning Empowered Natural Language Understanding Voice...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jayalakshmi, V. Deepa, R. Ganesan, R. Sriram, N. Surendran, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram Tamilnadu Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Computer Science and Engineering chennai India Kg College of Arts and Science Department of Computer Applications Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The 'Kissan Samvaad' work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers. © 2024 IEEE.

关键词： agriculture chatbot kissan samvaad natural language understanding (nlu) RASA RNN

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Cognition2Vocation: meta-learning via ConvNets and continuous transformers

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Neural Computing and Applications 2024年第21期36卷 12935-12950页

作者： Kamran, Sara Hosseini, Saeid Esmailzadeh, Sayna Kangavari, Mohammad Reza Hua, Wen School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing The Hong Kong Polytechnic University Hong Kong

Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. Accordingly, the enterprises aim to hire the most suitable person from a massive array of similar applicants, maximizing performance and minimizing the gap between strategic indicators and predefined targets. While cognitive factors signify the best-suited person from similarly skilled workers, inferring pertinent latent cues from noisy and growing social network contents is time-intensive. To tackle the challenges involved, we propose a framework that, on the one hand, extends influential features based on the correlations between cognitive cues and, on the other hand, leverages a novel continuous transformer to mitigate the overlapping and approximation issues in discrete modeling. Rather than relying on discrete patterns that may evolve frequently, we use continuous elements that include not only numerous aggregating components but also sense minor irregular fluctuations. In a hybrid manner, we fuse multiple base models to transfer a higher representation to the meta-learning unit, agglomerating outputs from gradient boosters and the ConvNets. The experimental results show that our proposed framework can outperform trending vocation estimation methods by 1.36% in F1-Score and approximately 1% in accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Knowledge management

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Advanced Techniques for Early Prediction from CT Scans 4

Advanced Techniques for Early Prediction from CT Scans

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sachdeva, Vrinda Mishra, Nitin Kumar, Arun Singh, Neeru G.L Bajaj Institute of Technology and Management Department of Computer Science Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India G.L Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India

ISBN: (纸本)9798350364729

Visual representation holds significant significance in the healthcare industry. Healthcare professionals previously utilized medical imagery to accurately diagnose diseases and offer food to patients. Healthcare professionals occasionally encountered difficulties in diagnosing operations as a result of intricate medical imaging. Cancer is a noncommunicable disease that is difficult to detect in its early stages. The death rate of human beings rises annually as a result of inaccurate diagnosis. The researchers are encountering a significant obstacle in the analysis of medical imagery connected to cancer. The early detection of cancer is a significant obstacle that must be overcome. Therefore, it is imperative to preprocess medical images in order to detect important aspects. This study applies various image preprocessing algorithms to medical images of lung cancer. An analysis is conducted using a comparison-based approach. © 2024 IEEE.

关键词： Lung cancer

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Carbon Dioxide Emission Forecasting using Machine Learning and Time Series Statistical Models 25

Carbon Dioxide Emission Forecasting using Machine Learning a...

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25th International Arab Conference on information technology, ACIT 2024

作者： Hassan, Tahir Mansour, Khalid Waseemullah, Waseemullah Memon, Muhammad Qasim Ned University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Arab Open University Faculty of Computer Studies Bahrain Sindh Bhitshah Pakistan

ISBN: (纸本)9798331540012

Climate change is considered a global disaster that has wreaked havoc worldwide. Climate change conditions are primarily driven due to emission of carbon dioxide and other greenhouse gases. Around the globe, several countries are facing serious challenges related to global warming and climate change. The adverse effects of global warming and climate change are recently observed in the form heat waves, floods, extreme rainfall events and drought conditions. The escalating CO2 emission level causes threats of food security and water scarcity. To overcome this, timely and accurate CO2 emissions forecasting is essential for developing effective mitigation strategies in such a manner that CO2 emission impacts can be reduced. The aim of this study is to forecast the total CO2 emissions of upcoming years using various statistical, machine learning models such as ARIMA (Auto-Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Linear Regression, and Decision tree regressor. We had forecasted CO2 emission on the basis of influencing factors, which highly contributes in CO2 emission. The performance of the models is validated using multiple evaluation metrics such as MAE, MSE RMSE,MAPE and R2 square. The results show that CO2 emission is highly affected by fossil fuel consumption. In particular, Linear regression performs better among all applied models, with R2 value 0.93 and MAE, RMSE and MAPE values 5.52, 6.47 and 0.03, respectively. Our research delineates the results and insights that will assist concerned authorities, policymakers and environmental agencies. The present study also aim to inform authorities, helping them to better understand the future of CO2 emission trajectories and make timely efforts and interventions to tackle the climate change and global warming challenges. © 2024 IEEE.

关键词： Low emission

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A Novel Channel Selection and Classification in Motor Imagery for Brain computer Interface Using Meta Heuristic Algorithms

A Novel Channel Selection and Classification in Motor Imager...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Devarakonda, Nagaraju Kamarajugadda, Raviteja Anandarao, Sarvani School of Computer Science and Engineering VIT-AP University Amaravathi India Department of Information Technology Lakireddy Bali Reddy College of Engineering Mylavaram India Department of Computer Science and Engineering SRM University AP Amaravati India

ISBN: (纸本)9789819613472

A vital input for a task using the brain in BCI (Brain computer Interface) applications is the motor imagery (MI) signal from the brain. Users of BCI systems can operate external equipment by using their brain activity, using motor imagery as a control method. Innumerous Electroencephalography (EEG) channels are used to gather nerve impulses from the brain, which are the most prevalent input for Brain computer Interface systems while they are minimally invasive, flexible, and low in price. The computational overhead is increased by multichannel BCI systems’ high-dimensional data, which causes processing to be slower and to cost more money. EEG details are regularly gathered from over 100 different brain regions;therefore, it is essential to use channel selection algorithms to choose the ideal channels for a given circumstance. However, the primary objective of choosing the channel in EEG data analysis is to lessen the computer intricacy, improve the precision of classification by eliminating over fitting, and save setup time. In this study, we suggested a remora optimization technique that was inspired by nature to lessen the computational load brought on by several channels. Using predetermined criteria, a number of channel selection evaluation techniques, whether classification-based methods used or not it extracted the proper channel subsets. In order to determine the greatest classification accuracy, the classification procedures were utilized in the end. Three publicly available EEG datasets are used to validate the experiment (BCI Competition IV-1,2a, Competition III-3a), and it resulted in superior classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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Beyond Theory: Investigating the Practical Feasibility of Confidential Computing 20

Beyond Theory: Investigating the Practical Feasibility of Co...

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20th International Conference on Frontiers of information technology, FIT 2023

作者： Ettesvoll, Edward Rognerud Schule, Leon Camill Mudassar Yamin, Muhammad Imran, Ali Shariq Norwegian University of Science and Technology Department of Information Security and Communication Technology Gjovik Norway Norwegian University of Science and Technology Department of Computer Science Gjovik Norway

ISBN: (纸本)9798350395785

Confidential Computing (CC) is a fairly new concept encompassing technologies designed to safeguard data during processing, playing a crucial role in data protection throughout its lifecycle. In this paper, we address the problem of ensuring data security from a technical perspective, delving into the intricate workings of technologies enabling CC. While the initial sections focus on establishing fundamental concepts related to CC, including Trusted Execution Environments (TEEs), later parts of the paper present practical prototypes demonstrating the setup of a CC environment on Cloud Computing Platforms (CCPs), such as Microsoft Azure. We specifically showcase two leading technologies in this domain: Intel Software Guard Extensions (SGX) and AMD Secure Encrypted Virtualization (SEV), both harnessing dedicated Central Processing Units (CPUs) from their respective manufacturers. This paper extensively covers the diverse approaches these technologies employ to secure data in use. Additionally, we offer a brief comparative analysis of related technologies as alternatives. Our research not only addresses the problem statement and motivation but also provides insights into significant results achieved in the realm of CC. © 2023 IEEE.

关键词： Program processors

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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Risk Assessment in Agile Software Development 11th

Risk Assessment in Agile Software Development

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11th International Symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Ghosh, Oindrilla Chaki, Rituparna Chaki, Nabendu Cortesi, Agostino Department of Computer Science S. A. Jaipuria College Kolkata India A. K. Choudhury School of Information Technology Kolkata India Department of Computer Science and Engineering University of Calcutta Calcutta India DAIS Ca’Foscari University Venice Italy

ISBN: (纸本)9789819797615

Risk assessment in software engineering has seen many approaches. Despite the amount of scientific literature on risk management, the failure rate of software projects after the first installation remains considerable. The reason is probably that the dynamically changing software feature has not been imbibed in the framework, which mostly deals with risk management in retrospect. This article attempts to suggest a new approach which will take these gaps into consideration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agile methodologies Risk management Software development

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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