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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Sathishkumar A. Karunamurthy S. Saint Jesudoss E. Anusri C. Ugamaalya S. Valarmathy Dept. of CSE Manakula Vinayagar Institute of Technology Puducherry Master of Computer Applications Sri Manakula Vinayagar Engineering College Puducherry Dept. of CSE Christ Institute of Technology Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Tuberculosis (TB) is a serious airborne infectious disease with a high fatality rate, primarily affecting the lungs. Early detection is vital for effective treatment, yet the current healthcare landscape suffers from a significant shortage of medical professionals and screening facilities, which hinders timely diagnosis. The lung cancer pandemic has further exacerbated these challenges, straining healthcare systems and complicating diagnostic procedures for respiratory diseases. Existing diagnostic systems predominantly rely on the ResNet algorithm to predict TB. However, these systems often fall short when it comes to accurately diagnosing COVID- 19, leading to reduced prediction accuracy and missed opportunities for early intervention. In light of these limitations, this study proposes a novel hybrid approach that combines Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to facilitate the simultaneous prediction of both TB and LUNG CANCER. The integration of RNNs allows for effective temporal analysis of sequential data, while CNNs excel in extracting spatial features from medical imaging, such as X-rays or CT scans. This synergistic model is designed to improve the predictive accuracy for both diseases by leveraging the strengths of each architecture. By enhancing feature extraction and analysis capabilities, this innovative approach aims to address the pressing need for reliable diagnostic tools in the face of inadequate healthcare resources. Ultimately, the future system has the efficiency to enable earlier diagnoses and the treatment outcomes, and contribute to better public health management during dual outbreaks of TB and lung cancer.

关键词： Accuracy Recurrent neural networks Tuberculosis Pulmonary diseases Lung cancer Pressing Feature extraction Prediction algorithms Reliability Public healthcare

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MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics

arXiv

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arXiv 2025年

作者： Jin, Haoan Shi, Jiacheng Xu, Hanhui Zhu, Kenny Q. Wu, Mengyue Ant Group Hangzhou China SJTU Shanghai China SJTU: X-LANCE Lab Dept. of Computer Science and Engineering Shanghai Jiao Tong University China FDU: Institute of Technology Ethics for Human Future Fudan University China UTA: Dept. of Computer Science and Engineering University of Texas Arlington United States

Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models’ grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs’ ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts. © 2025, CC BY.

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A Threat modeling approach to analyze and mitigate WhatsApp attacks: A Review

A Threat modeling approach to analyze and mitigate WhatsApp ...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Pawan Kumar Patidar Deepak Singh Tomar R.K. Pateriya Yogesh Kumar Sharma Dept. of Computer Science Engineering Maulana Azad National Institude of Technology Bhopal India

One of the most popularly used features on smart-phones is WhatsApp. It is a free messaging app available for An-droid, IOS, and all other smartphones. A WhatsApp vulnerability is a hole or a weakness in the app, which can be a design flaw or an implementation bug. Through bug, an attacker can enter into the app and cause harm to the database of an application. A database contains private information like backup files, chatting information, contacts, etc. The most dangerous vulnerability in WhatsApp are Authentication, Account Hijacking, and Message Manipulation. In these vulnerabilities, an attacker can manip-ulate the message but not hijack the entire account. There are some basic requirements for a secure and privacy-preserving chat service, database backup, encrypted database, etc. The awareness about the vulnerability of WhatsApp and its security settings in new versions of WhatsApp. Use WhatsApp security features and secure the services of the application. This paper aims to be aware of the risks and vulnerabilities of WhatsApp and use the Threat modeling method to mitigate its vulnerability. It used threat Modelling steps to help organizations to quantify risks and vulnerabilities, ensuring those that need the most attention and resources do so to minimize their attack surface in a purposeful way.

关键词： Threat modeling Freeware Databases computer bugs Organizations Security Cryptography

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Unified Object Detection Model for Image Style Convertible Modalities

Unified Object Detection Model for Image Style Convertible M...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Mu-Chuan Li Xiu-Zhi Chen Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results.

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A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design 13

A Survey of the AVOA Metaheuristic Algorithm and its Suitabi...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Sabo, Aliyu Odoh, Theophilus Ebuka Habu, Samuel Shahinzadeh, Hossien Ebrahimi, Farshad Department of Electrical AndElectronic Engineering Serdang Malaysia Water Resource Institute Department of Electrical Engineering Kaduna Nigeria Amikabir University of Technology Department of Electrical Engineering Tehran Iran University of Houston Dept. of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350330151

The performance of metaheuristic optimization techniques is system-dependent, it is essential to ascertain the suitability of an algorithm before its application. This study surveys the suitability of AVOA in damping controller design by reviewing the performance of AVOA in used cases of power system optimization problems and damping controller design. Based on the findings, AVOA is recommended as a good optimizer in power system optimization and damping controller design. © 2023 IEEE.

关键词： Damping

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms 4

Two-Area Power System with Automatic Generation Control Util...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sati, Madan Mohan Kumar, Dinesh Singh, Akhilesh Raparthi, Mohan Alghayadh, Faisal Yousef Soni, Mukesh Graphic Era Hill University Deptt. of Mathematics Bhimtal263136 India Srm Institute of Science and Technology Dept. of Computer Science and Engg. UP Ghaziabad201204 India Uttarakhand Technical University Campus Institute Deptt. of Electrical Engg. Pithoragarh India Alphabet Life Science Software Engineer DallasTX75063 United States Almaarefa Univ Riyadh College of Applied Sci Computer Science and It Department Saudi Arabia Dr. D. Y. Patil Vidyapeeth School of Science and Technology Pune India

ISBN: (纸本)9798350343670

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications. © 2024 IEEE.

关键词： and TD (Transmission Delay ANN BBBC FOPID GDB (Generation Demand Balance) GRC (Generation Rate Constraint) Load Frequency Control (LFC) PID PSO

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Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryogenic Applications

Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryo...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Zachary Cochran Maher Rizkalla John J. Lee Trond Ytterdal Linknath Surya Balasubramanian dept. of Electrical and Computer Engineering) Indiana University Purdue University at Indianapolis Indianapolis USA dept. of Electrical and Computer Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798350338782

ISBN: (纸本)9798350338799

Cryogenics amplifiers feature very low noise injection and low power consumption, and therefore, they are of highly demand in RF imaging, space communications, medical applications, and quantum computing. Unconditional stability with high gain also feature these applications. The band gaps of these devices are independent from temperature, suggesting their suitability for cryogenics applications. Furthermore, the nanometer scale lengths of these devices lead to ballistic transport with no scattering or diffusion, resulting in high-speed, low-power, and low noise capabilities within Extremely High Frequency (EHF) operating range that may extend into the THz. This study addresses these parameter issues within the range of 90K to 300K, assuming liquid nitrogen coolant medium, and further shows its stability above the room temperature as well. An appropriate model for the device that incorporates complex parasitics was investigated within this temperature range. The device with its estimated parameters, has been integrated into a simulated three-stage amplifier system. A low-noise gain between 16.9-18.9 dB with estimated noise figure of 2.97 dB and a bandwidth of 269 GHz bandwidth were predicted, with a high Stern stability factor suggesting unconditional stability over the bandwidth.

关键词： Bandwidth Power system stability Stability analysis Nanoscale devices Mathematical models Thermal stability Gain

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Efficient Model for Multiview classification for diagnosis of Brain Tumors.

Efficient Model for Multiview classification for diagnosis o...

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Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management (IC-RVITM), International Conference on th

作者： B Anil Kumar B M Chandrakala B V Shruthi Dept of Computer Science & Engineering RV Institute of Tech & Management Bangalore Dept of Information Science & Engineering Dayananda Sagar College of Engineering Bengaluru Dept of Computer Science & Engineering Nitte Meenakashi Institute of Technology Bangalore

Brain tumors are caused mainly by DNA abnormalities, which result in aberrant cell division, prolonged cell lifespans, and the creation of aggregative masses inside the brain. Such tumors prevent the brain from operating at its best, sometimes resulting in long-term problems. These tumors’ genesis (primary or secondary) and malignancy (benign or malignant) are used to classify them. While some might not be immediately lethal, their growth or contact with healthy brain tissue might cause damage. Investigations into various approaches, such as conventional medical imaging and artificial intelligence (AI), mainly using deep learning (DL) and transfer learning (TL), is prompted by efforts to identify and categorize brain tumors. Implementing the EfficientNetB0 model, which is famous for its adaptability and efficacy, is an excellent demonstration of this strategy. This work uses a well-curated dataset of 3264 photos from various classes to closely analyze the use of the EfficientNetB0 TL model to identify and classify brain tumors from MRI scans. The model’s adaptability and accuracy are meticulously examined using a dual-pronged strategy incorporating transfer learning and fine-tuning, yielding an astounding accuracy of 98.5%. Accuracy and loss charts illustrate the model’s learning development in vivid detail, and a well-defined confusion matrix provides extensive insights into categorization effectiveness across various tumor classifications. The achievement of a 98.5% accuracy rate highlights the potential paradigm change made possible by AI and TL in the diagnosis and classification of brain tumors. This accomplishment confirms the efficacy of such sophisticated procedures and promises to speed up diagnosis, enable prompt therapies, and improve patient prognoses. The combination of AI and TL heralds a new era of precision medicine, ready to reshape the field of brain tumor care as research in this area grows.

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Design of Arrow Shaped Microstrip Patch Antenna for X and Ku- Band Applications 1

Design of Arrow Shaped Microstrip Patch Antenna for X and Ku...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Yuvaraj, D. Nageswara Rao, M.V. Shanmuga priya, P. Naveneetha krishanan, S. Department of Computer Science Cihan University Kurdistan region Duhok Iraq Dept. of ECE GMR Institute of Technology RajamSrikakulam Andhra Pradesh India Department of Computer Science and Engineering M.I.E.T Engineering College Trichy India Department of Electronics and Communication Engineering SACS MAVMM Engineering College Madurai India

ISBN: (纸本)9781607685395

A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. The suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. Initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. Then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher *** measurement of the antenna is 12 x 15 x 1.6 mm3. The antenna parameters are optimized using FEM based HFSS software. The operating range of frequencies include from 6.75 GHz to 18.96 GHz. The proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. Simulated S11 parameters, VSWR, Gain and current distributions are offered in this paper and S11 simulated parameters are in good agreement with measured parameters. © The Electrochemical Society

关键词： Arrow shaped Ku band Printedmicrostrip antenna X band

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Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

arXiv

引用

arXiv 2024年

作者： Wu, Man Zheng, Xin Zhang, Qin Shen, Xiao Luo, Xiong Zhu, Xingquan Pan, Shirui The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Monash University Melbourne Australia The College of Computer Science and Software Engineering Shenzhen University Guangdong China The School of Computer Science and Technology Hainan University Haikou China The Dept. of Electrical Engineering & Computer Science Florida Atlantic University United States Griffith University QLD Australia

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area. © 2024, CC BY.

关键词： Graph neural networks

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