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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kumar, N. Shantha Karthikeyan, S. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Electrochemical energy based smart industrial automation uses electrochemical energy generated from chemical reactions to power and control industrial processes. This energy is generated through the use of electrolyte and electrodes, which convert chemical energy into electrical energy. This energy can then be used to power and control industrial machines, such as industrial robots and automated assembly lines. Additionally, this energy can be used to monitor, regulate, and control processes, such as temperature and pressure, within an industrial environment. This technology offers numerous advantages over traditional methods of powering and controlling industrial operations. Firstly, it is more efficient and cost effective than traditional methods. Secondly, it has the potential to reduce energy consumption, improve efficiency, and increase productivity. Finally, it is environmentally friendly, as it does not produce harmful emissions or waste materials. The use of electrochemical energy based smart industrial automation is the way of the future for industrial operations. The potential of this technology is immense, and the advantages it can bring to industrial operations are numerous. With the right implementation and application, this technology has the potential to revolutionize how industrial processes are operated and managed. © 2023 IEEE.

关键词： Electrolytes

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Few-Shot Lung Cancer Classification Using Prototypical Networks 4

Few-Shot Lung Cancer Classification Using Prototypical Netwo...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Amarasinghe, Nimesha Hansani Ambegoda, Thanuja D. University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798350384864

Lung cancer, a leading cause of mortality globally, demands early and accurate detection to improve patient out-comes. Current diagnostic methods, primarily relying on CT scans, face challenges in lung cancer subtype identification, particularly due to the scarcity of extensive medical image datasets for each subtype. This study addresses this limitation by leveraging prototypical networks, an innovative few-shot learning approach, which excels in scenarios with limited data. The proposed method capitalizes on a small number of samples per category, integrating a pre-trained model for feature extraction from lung CT scans. We rigorously evaluated the model's performance, focusing on its accuracy relative to the sample size per category. Remarkably, the method achieved a 98% accuracy rate after 15 epochs, showcasing its efficacy. This research not only confirms the feasibility of using prototypical networks for lung cancer subtype classification but also opens new avenues for applying few-shot learning techniques in medical imaging. Our findings hold significant potential for enhancing lung cancer diagnostics, thereby contributing to improved patient care and survival rates. © 2024 IEEE.

关键词： computerized tomography

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Detecting and Mitigating the Weakest Cybersecurity Link in an Information System 23

Detecting and Mitigating the Weakest Cybersecurity Link in a...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Sadeghian, Ali Mejri, Mohamed Department of Computer Science and Software Engineering Laval University Quebec Canada

ISBN: (纸本)9781643685380

In today's digital landscape, the prevention of cyber attacks has become exceptionally crucial. This is especially true for safety-critical systems, where safeguarding against these threats is of paramount importance. To address this concern, the MITRE Corporation has developed ATT&CK, an extensive framework comprising data matrices. This framework serves the purpose of assessing a company's security preparedness and pinpointing vulnerabilities that may exist within its infrastructure. By leveraging the capabilities of MITRE ATT&CK, including its tactics and techniques, in conjunction with the LDA4CPS tool, we have devised a novel approach to identify the most critical vulnerabilities in a susceptible system. Furthermore, incorporating MITRE ATT&CK mitigations tailored to the discovered vulnerability empowers the blue team (defensive side) with tangible, practical measures to fortify their security posture. This approach enhances their capability to effectively counter cyber threats, bolstering their overall defensive capabilities. © 2024 IOS Press. All rights reserved.

关键词： Cyber attacks

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Compilation of Text to Video models 2

Compilation of Text to Video models

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Anupama, Y.K. Neha, Neelam Verma, Dinky Sankeerthana, S.B. Jha, Medha Dayananda Sagar College of Engineering Department of Computer Science Bangalore India

ISBN: (纸本)9798350368109

several approaches are available for generating videos from the given text prompt. The existing methodologies encompass Generative Adversarial Networks (GANs), Transformer architectures, Variational Auto encoders (VAEs), Stable Diffusion techniques, and 3D Convolutional Neural Networks (3D CNNs) are not up to the mark. In this research article methodologies explored are Sora, Tune-a-video, Cog Video, and ModelScopeT2V. Out of these ModelScopeT2V performs in a better way leverages stable diffusion and transformers to generate semantically coherent videos directly from text. It processes input text, extracts features, refines latent space representation, and converts it into video frames. Spatiotemporal blocks and attention mechanisms ensure both visual appeal and temporal consistency in the generated videos. © 2024 IEEE.

关键词： Generative adversarial networks

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Forensic Analysis and Detection of Illicit Transactions in Bitcoin Network 5

Forensic Analysis and Detection of Illicit Transactions in B...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ayoob, Fiyan Mehfil Jose, Melvina Kumar, S. Udhaya Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331504403

Blockchain technology, celebrated for its decentralized architecture and promise of transparency, has also become a conduit for illicit activities. This paper introduces a novel rule-based methodology for detecting illegal transactions within blockchain networks. By analyzing transaction data through parameters such as block range, transaction hashes, and addresses, the approach identifies suspicious patterns including high-value transactions, unusual fee structures, and links to known illicit addresses. Implemented in Python, the methodology has been rigorously tested and validated, achieving an accuracy of 88% with strong precision, recall, and F1 scores. This research advances the field of cybersecurity, regulatory compliance, and law enforcement by providing a practical and computationally efficient framework for detecting illegal activities in Bitcoin transactions. The rule-based framework, utilizing both threshold-based and heuristic rules, offers valuable tools for financial institutions, law enforcement, and regulators to identify and investigate illicit transactions effectively. The study underscores the need for proactive measures in protecting digital ecosystems and proposes future enhancements, including advanced anomaly detection and realtime analysis, to further improve the framework's efficacy and adaptability. © 2024 IEEE.

关键词： Bitcoin

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Green hydrogen production from photovoltaic power station as a road map to climate change mitigation

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Clean Energy 2024年第2期8卷 156-167页

作者： Kehinde Ridwan Kamil Bassey Okon Samuel Umar Khan Department of Computer Science and Engineering Edge Hill UniversityUK Enel Green Power Rome Italy Materials Research Laboratory Steelagon Engineering LimitedKaduna StateNigeria

The increasing recognition of hydrogen as a critical element in the global net-zero transition and its clear role in decarbonizing challenging sectors coincide with the growing urgency to address climate ***'s favourable renewable-energy capacity,ranging from 28%to 36%for solar,has been reported by the global solar irradiance ***,the majority of hydrogen production today relies on fossil fuels(96%),with only a small fraction(4%)being produced through water *** though there have been many studies on climate change mitigation with a focus on Africa,a green hydrogen production from a photovoltaic power station approach has not been ***,literature with a focus on Nigeria is *** study focuses on the African green hydrogen production industry,utilizing Nigeria as a case study to explore the feasibility of generating clean hydrogen vectors from a percentage of photovoltaic power output in various regions of the country through stand-alone solar grid electrification *** of the usage and effectiveness of the produced hydrogen fuel in each region are carried out,with the highest region having an annual output of 12247278 kg of green hydrogen and 8573094 kg of ammonia and the lowest region having an output of 511245 kg of green hydrogen and 357871 kg of ammonia,and the expected production from the proposed usage of 50%of the power generation output of the installed 1.6-MWp and 80-kWp solar power minigrids in the regions is *** analyses were repeated for the other considered regions in the *** results showcased the enormous advantages of the electrolytic production of hydrogen and how the greener economy project can play a major role in mitigating climate change effects and overreliance on fossil fuels as the driver of the economy in many African countries.

关键词： renewable energy photovoltaic climate change green hydrogen vector fossil fossil fuels decarbonization electrolysis

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BERT-based RNN for Effective Detection of Depression with Severity Levels from Text Data 8

BERT-based RNN for Effective Detection of Depression with Se...

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8th IEEE Symposium on Wireless Technology and Applications, ISWTA 2024

作者： Saraswat, Pavi Beniwal, Rohit Department of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9798350351354

Due to a lack of early and accurate treatment, a large number of populations are suffering from depression. The posts available on online digital platforms play major role in providing useful signs concerning mind state of a user. The traditional depression methods involve a large amount of time, budget, and expertise, thus, making depression detection a complicated task. Therefore, there is a critical need to propose a computer-assisted system to analyze and detect depression efficiently at different severity levels. Focusing on such concerns, this study performed multi-class classification utilizing a publicly available text dataset to categorize users into non-depressed, moderately, and severely depressed. This study presented a hybrid technique that robustly combines Bidirectional Encoder Representations from Transformers (BERT) with two versions of Recurrent Neural Network (RNN) i.e. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The BERT model is implemented to extract useful word embeddings which are then fed to RNN comprising LSTM, dropout, and GRU layers. The findings revealed that the employed approach outperformed all other employed models and techniques in the past studies by attaining an accuracy of 95.0%. Thus, the presented strategy is expected to be beneficial for clinicians to perform depression detection at different stages accurately. © 2024 IEEE.

关键词： Budget control

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Digital Sharecropper: Connecting Landowners, Farmers, and Investors Through Blockchain 26

Digital Sharecropper: Connecting Landowners, Farmers, and In...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ali Sultan, Md Omar Md Arif Shahriar, Kazi Islam Shanto, Md Hasibul Sadek Ferdous, Md Shahjalal University of Science and Technology Department of Computer Science & Engineering Sylhet Bangladesh BRAC University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

The agricultural sectors of many countries are dealing with difficulties such as underused arable land and inadequate infrastructure as a result of insufficient investment, posing serious concerns to food supplies and overall economic growth. To meet the increasing food demand and mitigate the aforementioned challenges, we propose a blockchain empowered smart agriculture system that connects farmers, landowners, and investors to provide farmers with access to land and financial resources. By leveraging blockchain technology, the system streamlines land sharing and crowdfunding for profit, facilitating transparent and accountable relationships between all entities. In this paper, we present the developed system on our proposal, discuss its architecture and illustrate its use-case to show its applicability. Finally, we analyse the performance of the developed system using metrics such as latency and response time. © 2023 IEEE.

关键词： Agriculture Blockchain Distributed System Hyperledger Fabric Private Blockchain

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NeuroSense: Smartwatch-based Early Detection Framework for Alzheimer’s Disease 22

NeuroSense: Smartwatch-based Early Detection Framework for A...

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Dixit, Aarju Das, Debasis Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331510183

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with an annual global economic impact of approximately $1 trillion. Early diagnosis is crucial to mitigate disease progression, yet current detection methodologies lack consistency and accuracy in delineating disease stages. To address these challenges, we introduce NeuroSense, a novel smartwatch-based early detection framework for AD. NeuroSense employs advanced activity recognition algorithms to passively monitor ambulatory patterns, behaviors, and sleep cycles via Inertial Measurement Unit (IMU) and photoplethysmogram (PPG) sensors from smartwatches (i.e., Apple Watch and Fitbit), including metrics like walking speed, sleep posture, and heart rate variability. NeuroSense leverages enhanced shallow neural networks integrating cross-sectional entropy to dynamically adjust feature weights, improving sensitivity to subtle variations in AD biomarkers. Additionally, the SqueezeNet architecture has been optimized by introducing deeper fire modules and expanding filter sizes, enabling high-resolution feature extraction with reduced computational overhead. These models are further refined with a cloud computing infrastructure, allowing model updation, ensuring adaptive learning based on continuous data influx. To validate its efficacy, NeuroSense was tested on AI-generated datasets, achieving a detection accuracy of 94.4%, substantially higher than existing approaches. This novel framework provides a robust, scalable, and non-invasive solution for continuous AD monitoring, representing a significant advancement in the early detection and management of neurodegenerative disorders. ©2024 IEEE.

关键词： Sleep research

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