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14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Huang, Jiahuan Shen, Wei Yu, Jie Liu, Qiang Hu, Tao Wei, Jianghao Duan, Xiaoqiu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Province Wuhan430205 China

ISBN: (纸本)9798350361889

To address the storage capacity limitations and high costs inherent in the existing single-chain storage model of blockchain-based traceability systems, a strategy combining on-chain and off-chain storage using IPFS as an alternative storage architecture is proposed. The goal is to enhance the storage model while ensuring data storage stability. Storing frequently accessed information off-chain helps alleviate the excessive pressure on the blockchain caused by high-frequency queries, while ensuring the security and immutability of on-chain data. Additionally, IPFS technology expands storage capacity and reduces storage costs through its distributed storage, thereby improving data storage and retrieval efficiency. Experimental validation shows that compared to traditional single-chain traceability models, this storage optimization approach can reduce data storage time by 8.05% and data retrieval time by 30.97%. © 2024 IEEE.

关键词： Blockchain

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Multimodal Information Extraction:A systematic Review of subtask, modal types and applications based on Deep Learning in Banking sector 5

Multimodal Information Extraction:A systematic Review of sub...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Srinivsan, Samundiswary Krishna, R. Sathya Bama Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India SIES Graduate School of Technology Navi Mumbai India

ISBN: (纸本)9798350361155

Deep Learning has been widely applied in all application domains namely Healthcare, Entertainment, Banking & finance, Agriculture, Aerospace & defence etc. Due to stupendous achievement of deep learning as a data processing technique has fascinated in all research areas. Handling of Deep learning in Banking and Financial sector is pervasive. Advancement of Information extraction especially because of different formats of information such as text, image, sound, video can be extracted simultaneously by the usage of deep learning techniques. This study reviews and analyses multimodal information extraction based on deep learning techniques especially on banking and financial sector. It aims to enhance the efficiency of document-intensive business tasks in the financial sector. Deep review analysis will be carried out to understand subtasks of Information extraction and techniques involved in Banking domain. Finally, how multimodal information extraction using deep learning enhances the efficiency than unimodal and its use cases in current AI based automation world will be highlighted. © 2024 IEEE.

关键词： Finance

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The Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen 4th

The Survival Analysis of Mental Fatigue Utilizing the Esti...

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4th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Eswar Reddy, R. Santhi, K. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031488870

The aim of this study is to investigate mental fatigue using the Kaplan-Meier and Nelson-Aalen estimators in survival analysis. Mental fatigue is a common occurrence when the mind becomes tired from regular tasks, and it can have a negative impact on an employee’s operational functions and job efficiency. To detect mental fatigue, the shallow Kaplan-Meier method is employed by analyzing data from employee burnout evaluations. Both the Kaplan-Meier and Nelson-Aalen estimators have proven to be effective in automatically analyzing various features from raw data. However, they often impose a significant burden on system resources during training and predictions. Therefore, alternative methods of analysis are necessary to derive the survival curve. In this paper, we provide a mathematical foundation for the Kaplan-Meier method and explain the concept of censoring, including right censoring, interval censoring, and left censoring. Furthermore, we construct a Kaplan-Meier survival curve, which represents the probability of survival over time. The Kaplan-Meier survival curve is considered the most reliable and is recommended for predicting the variable under investigation, particularly in the fields of public health and medical research. The findings of this research can also be utilized to develop interventions and strategies aimed at reducing mental fatigue and improving employee morale. Enhancing employee morale can positively impact an organization as a whole, as mental fatigue has been associated with lower job satisfaction and an increased likelihood of employee turnover, both of which can be further explored in future studies. Overall, this study sheds light on the significance of understanding and addressing mental fatigue in the workplace, and it provides valuable insights that can contribute to the well-being of employees and the success of organizations. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Job satisfaction

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Multi-publisher and Subscriber Lightweight Communication for IoT Using the Cloud 5th

Multi-publisher and Subscriber Lightweight Communication for...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Trivedi, Atindra Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819719228

The rapid proliferation of IoT devices has spurred the need for efficient and scalable communication protocols that enable seamless data exchange between IoT sensors and cloud-based platforms. This paper delves into the realm of multi-publisher and subscriber lightweight communication for IoT, leveraging the capabilities of the MQTT protocol. In the context of the IoT, sensors play a pivotal role as data collectors, extracting valuable insights from the physical world. This work elucidates the process of connecting these sensors to the cloud, emphasizing the significance of MQTT as the communication bridge. The MQTT protocols publish–subscribe model fosters a decoupled communication paradigm, allowing IoT sensors to act as publishers and cloud-based entities as subscribers. This architecture not only minimizes resource consumption but also accommodates the growing number of IoT sensors while ensuring efficient data transmission and real-time analysis. Through a comprehensive exploration of the interplay between IoT sensors, cloud connectivity, and MQTT communication, this paper sheds light on the potent synergy driving the evolution of IoT ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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MOPSO-driven optimization for sustainable retrofitting: balancing time, cost, and environmental impacts

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Asian Journal of Civil engineering 2025年第5期26卷 2165-2178页

作者： Singh, Rekha Sangeeta, Viswanadham Manjunath, T.C. Singh, Prachi Bishoyi, Sasanka Sekhar Pati, Aditya Kumar Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science & Engineering Rajarajeswari College of Engineering Bengaluru India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India Department of Mathematics NIST University Berhampur India Department of Mathematics Centurion University of Technology and Management Gajapati R. Sitapur India

Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) approach to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its performance with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： MOPSO Multi Objective optimization Sustainable construction practices Sustainable retrofitting Time-cost-environmental trade-off

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Convenient synthesis and delivery of a megabase-scale designer accessory chromosome empower biosynthetic capacity

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细胞研究（英文版） 2024年第4期34卷 309-322页

作者： Yuan Ma Shuxin Su Zongheng Fu Chu Zhou Bin Qiao Yi Wu Ying-Jin Yuan Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjinChina Frontiers Research Institute for Synthetic Biology Tianjin UniversityTianjinChina Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjinChina

Synthetic biology confers new functions to hosts by introducing exogenous genetic elements,yet rebuilding complex traits that are based on large-scale genetic information remains ***,we developed a CRISPR/Cas9-mediated haploidization method that bypasses the natural process of *** on the programmed haploidization in yeast,we further developed an easy-to-use method designated HAnDy(Haploidization-based DNA Assembly and Delivery in yeast)that enables efficient assembly and delivery of large DNA,with no need for any fussy in vitro *** HAnDy,a de novo designed 1.024 Mb synthetic accessory chromosome(synAC)encoding 542 exogenous genes was parallelly assembled and then directly transferred to six phylogenetically diverse *** synAC significantly promotes hosts'adaptations and increases the scope of the metabolic network,which allows the emergence of valuable *** approach should facilitate the assembly and delivery of large-scale DNA for expanding and deciphering complex biological functions.

关键词： designer empower accessory biosynthetic capacity chromosome convenient delivery megabase-scale synthesis

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Wearable Sensor-Based Gait Analysis: Advanced Feature Extraction Techniques 2

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Karthik Salian, Preethi K. Ranjith, H.D. Salian, Supriya Jayashree Shetty, K. Annapoorneshwari NMAM Institute of Technology Udupi Nitte India NMAM Institute of Technology Udupi Nitte India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Mangalore India

ISBN: (纸本)9798350383867

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-depth analysis, enabling effective feature extraction for comprehensive gait assessment. © 2024 IEEE.

关键词： Accelerometers

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Road Traffic Signal Management - A Case Study 11

Road Traffic Signal Management - A Case Study

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Singh, Vandana Singh, Pragya Phutela, Nidhi Grover, Priya Birla Institute of Technology Department of Computer Science & Engineering Jharkhand Ranchi India Pune India

ISBN: (纸本)9798350350357

This research paper presents an exhaustive analysis of road traffic management, emphasizing the critical interplay between road capacity and the flow of vehicles in contemporary urban environments. It explores strategies to mitigate traffic congestion, enhance safety protocols, and optimize traffic flow dynamics. Road capacity and vehicle flow are integral components of effective traffic management systems. Through a meticulous examination of a well-defined case study, this research aims to underscore the pivotal role of efficient road traffic management systems in ameliorating traffic-related intricacies and elevating overall transportation efficacy. The selected case study serves as an exemplar, offering profound insights into various methodologies and cutting-edge technologies deployed within the traffic management domain. By dissecting the complexities inherent in this case, this research advances our comprehension of successful traffic management paradigms and provides a strategic framework for addressing analogous challenges in other urban landscapes. The discernments drawn from this study are poised to inform future urban planning endeavours and policymaking initiatives, ultimately facilitating the creation of more sustainable and seamlessly integrated transportation ecosystems. © 2024 IEEE.

关键词： Roads and streets

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An optimal selection of virtual machine for e-healthcare services in cloud data centres

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International Journal of Cloud Computing 2022年第1期11卷 79-88页

作者： Prathap, R. Mohanasundaram, R. School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

In recent times, in the handling of health facilities, cloud computing performs a big part. Such title is electronic healthcare, used to enhance the efficiency of cloud healthcare. A virtual device for health services is a significant selection and placement factor and one of the cloud's problems. The data centre processes the medical application with huge concentrations. This would maximise the use of resources and reduce the time the cloud data centre requires to execute the medical request. Multiple methods for solving cloud-based problems are employed. In this article, a multi-target grey-wolf optimisation hybrid application (ROMGW) algorithm is provided to address healthcare requirements effectively in cloud data centres. The suggested algorithm has been evaluated in the cloud data centres and contrasted to the well-known VM algorithm benchmark. Furthermore, cloud performance effectiveness improves the cloud use of the electronic health services scheme. Inaccuracy, the hybrid algorithm is able to interact with customers at the highest rate. This is one of the leading designs that improves the use of resources for cloud health facilities. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Cloud computing

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Detection of Brain Tumors in MRI Images through Machine Learning, Deep Learning, Faster R-CNN & YOLOv5 1

Detection of Brain Tumors in MRI Images through Machine Lear...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Ghufran, Fayez Kumar, Ashutosh Kumar, Ashish Kaushik, Avneesh Saxena, Sandeep Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida201308 India

ISBN: (纸本)9798350356816

As the tenth greatest cause of death, brain tumors have become a major worldwide health problem in recent years. The fast growth of brain tumors, which results in a short life expectancy for patients if detection and treatment are delayed, underscores the necessity of tackling this issue. Artificial intelligence has shown to be quite helpful in helping radiologists discover tumors without the need for intrusive techniques. Using the Faster R-CNN and YOLOv5 algorithms, this comprehensive work presents two deep learning-based approaches for tumor identification and classification. As the fifth version of the You Only Look Once paradigm, the YOLOv5 method is used since it is less computationally demanding than previous models and is very efficient. A 9-layer convolutional neural network is the foundation of our recommended approach, which is intended to identify and categorize tumors according to their shape, texture, and geographic distribution. The Brain Tumor Segmentation - MRI dataset, which has been downloaded from Kaggle, is used in this work. It comprises fifteen hundred brain MRI scans with infections and the same amount without. With an accuracy of 98.21%, the CNN model surpassed conventional machine learning techniques in our testing. Comprehensive metric computations assist this feat, and graphic displays give a clear view of the final outcomes. These findings are important for clinical decision-making, providing clinicians with better diagnostic precision for brain malignancies. This survey highlights the significance of applying deep learning in medical imaging and provides insight into how YOLOv5 and Faster R-CNN algorithms may be used to speed up and improve the process of detecting brain cancers. The promising results illustrate the vital role that technology plays in tackling urgent medical challenges and open the way for further breakthroughs in the field of artificial intelligence and healthcare. © 2024 IEEE.

关键词： Convolutional neural networks

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