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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Shreeraksha Shivani K Varsha S N Yashoda K Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation.

关键词： Text analysis Computational modeling Soft sensors Predictive models Very large scale integration Natural language processing Real-time systems Bayes methods Wearable devices Robots

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Metaverse Interview Room Creation With Virtual Interviewer Generation Using Diffusion Model

Metaverse Interview Room Creation With Virtual Interviewer G...

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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Jongwook Si Seongeun Yang Daemin Kim Sungyoung Kim Dept. Computer AI Convergence Engineering Kumoh National Institute of Technology Gumi Korea Dept. Computer Engineering Kumoh National Institute of Technology Gumi Korea

Virtual interviews are an effective way to respond quickly to the changing trends of our time and adapt flexibly to the hiring processes of various organizations. Through this method, applicants have the opportunity to practice their interview skills and receive feedback, greatly aiding their job preparation. Additionally, experiencing a virtual interview environment that is similar to an actual one enables them to adapt more easily to a variety of new interview situations. This paper delves deeply into the virtual interview environment implemented by combining cutting-edge metaverse technology and generative AI. Specifically, it focuses on creating an environment utilizing realistic Diffusion models to generate interviewers, enabling the provision of scenarios that are similar to actual interviews.

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Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability

Research Square

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Research Square 2025年

作者： Osei, Isaac Aabaah, Iven Benjamin, Appiah Dept. Of Computer Science Ho Technical University Ho Ghana Dept. Of Information System and Technology C. K. Tedam University of Technology and Applied Science Kumasi Ghana

Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant privacy concerns. This project investigates the efficiency of Federated Learning (FL) frameworks in comparison to a conventional centralized AI model. We evaluated seven different state-of-the art Federated Learning frameworks to assess their performance in maintaining model accuracy and scalability. Among these, Per-FedAvg and FedOpt demonstrated efficiency compared to the cen-tralized framework. To further understand the performance of these models, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret their predictions, ensuring that they focus on medically relevant fea-tures. This project highlights that Federated Learning frameworks, specifically Per-FedAvg and FedOpt, offer promising alternatives to traditional AI methods by providing enhanced performance in lung cancer detection. © 2025, CC BY.

关键词： Federated learning

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Nowshin Tasnim Kazi Rifah Noor Mursalina Islam Mohammad Nurul Huda Iqbal H Sarker Dept. of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Edith Cowan University Perth WA Australia

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.

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Mass Flow Rate of the Flow of Steady-State Viscous Liquid from a Small Vessel into a Vertical Tube

Mass Flow Rate of the Flow of Steady-State Viscous Liquid fr...

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Smart information Systems and Technologies (SIST), IEEE International Conference on

作者： Bakhyt Alipova Dept. of Mathematical and Computer Modelling International Information Technology University Almaty Kazakhstan Dept. of Mechanical and Aerospace Engineering University of Kentucky Lexington KY US

The problem of calculating the mass flow rate of the flow of steady-state viscous liquid from a small vessel into a vertical tube is considered. Definite calculations of a complex tube with a moving liquid are given. The velocity of the liquid movement, the pressure change are calculated, the laminar property of the liquid movement in this case is confirmed. 1 1 This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP14871641).

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A Sustainable Cloud-Based COVID-19 Symptoms Detection System

A Sustainable Cloud-Based COVID-19 Symptoms Detection System

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Khandakar Rabbi Ahmed Towfika Salam Shafiun Miraz Rabeya Basri Anni Sumya Afrin Nishe MD Istiak Hasan Rial Adjunct Researcher Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Rajshahi Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Computer Science and Engineering Aditya College of Engineering (AOCE) Kakinada Andhra Pradesh India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This Research shows another technique for discovering COVID-19 manifestations relying upon the Internet of Things cloud administrations to address a more extended period deferral of inspecting the crowds of people who enter public or private offices, which can create a harmful environment for disease propagation. In this system, a naturally checking measure is proposed utilizing a viable examination began using NodeMCU (ESP8266), ultrasonic (SR-04), RFID (RC522), temperature sensor (DS18B20), pulse oximetry sensor (MAX30100), and ThingSpeak stage. NodeMCU is an open-source device that facilitates the transmission of data from the MAX30205 and MAX30100 sensors, which measure human temperature and pulse oximetry, to the cloud platform ThingSpeak; at that point, it warns to check the director employee at that moment the gathered information arrived at an essential worth that predetermined beforehand and consequently make a move to settle the present circumstance. Simultaneously, the cloud stage will give a graphical portrayal of the information to show it utilizing various observing implements, for example, (PCs, mobiles, and others). In section point, there will be an alarming system dependent on our necessary conditions that will be frightening if the situation crosses the cutoff. So, our system mainly used temperature and pulse oximetry sensors to find better results and detect COVID-19.

关键词： COVID-19 Temperature sensors Temperature measurement Cloud computing Temperature distribution Ultrasonic variables measurement Pulse measurements Pulse oximetry Artificial intelligence Radiofrequency identification

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Near-Optimal UAV Deployment for Delay-Bounded Data Collection in IoT Networks

Near-Optimal UAV Deployment for Delay-Bounded Data Collectio...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shu-Wei Chang Jian-Jhih Kuo Mong-Jen Kao Bo-Zhong Chen Qian-Jing Wang Dept. of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The rapid growth of Internet of Things (IoT) applications has spurred the need for efficient data collection mechanisms. Traditional approaches relying on fixed infrastructure have limitations in coverage, scalability, and deployment costs. Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative due to their mobility and flexibility. In this paper, we aim to minimize the number of UAVs deployed to collect data in IoT networks while considering a delay budget for energy limitation and data freshness. To this end, we propose a novel 3-approximation dynamic-programming-based algorithm called GPUDA to address the challenges of efficient data collection from IoT devices via UAVs for real-world scenarios where the number of UAVs owned by an individual or organization is unlikely to be excessive, improving the best-known approximation ratio of 4. GPUDA is a geometric partition-based method that incorporates data rounding techniques. The experimental results demonstrate that the proposed algorithm requires 35.01% to 58.55% fewer deployed UAVs than the existing algorithms on average.

关键词： Simulation Scalability Heuristic algorithms Switched mode power supplies Organizations Data collection Autonomous aerial vehicles

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Syed Riyyan Chand Uzair Ahmed T Mohammed Ismail B.P. Pradeep Kumar Syed Bilal Chand Chaitra P Dept. of Computer Science Design Atria Institute of Technology Bangalore India Dept. of Computer Science Engineering Atria Institute of Technology Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards.

关键词： Accuracy Standards organizations Organizations Syntactics Real-time systems Electronic mail Reliability Servers Usability Sustainable development

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Sentiment Analysis for Curriculum of Independent Learning Based on Naïve Bayes with Laplace Estimator

Sentiment Analysis for Curriculum of Independent Learning Ba...

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information technology Research and Innovation (ICITRI), International Conference on

作者： Amelia Ariefah Widodo Murien Nugraheni Dept. of Informatics and Computer Engineering Education State University of Jakarta Jakarta Indonesia Dept. of Information System and Technology State University of Jakarta Jakarta Indonesia

Curriculum changes have a significant impact on the field of education in Indonesia, especially in universities. This is because the curriculum is used as a means to achieve the goals of educational success as well as guidelines in the implementation of teaching at every level of education. This study aims to determine public sentiment regarding the implementation curriculum of independent learning (MBKM) through Twitter based on Naïve Bayes with Laplace Estimator. Tweet search results use the keywords “kurikulum mbkm” and “mbkm”. The data used in this study amounted to 2500 tweet data which will be divided into 2000 data as training data with details of 1000 positive tweets and 1000 negative tweets and 500 data as testing data. The results showed the performance of Naïve Bayes using Laplace Estimator which resulted in an accuracy value of 80.20%, precision value of 82.00%, and recall value of 79.00%. Meanwhile, the performance of Naïve Bayes without Laplace Estimator resulted in an accuracy value of 77.00%, precision value of 80.00%, and recall value of 74.00%. Naive Bayes with Laplace Estimator reached the highest value in all metrics.

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A Novel Gain Enhanced Folded Cascode OPAMP in 28nm CMOS technology

A Novel Gain Enhanced Folded Cascode OPAMP in 28nm CMOS Tech...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Kumar, Ravi Nagulapalli, Rajasekhar Vishvakarma, S.K. Indian Institute of Technology Indore Dept. of Electrical Engineering Madhya Pradesh India Oxford Brookes University Dept. of Electrical and Computer Science Oxford United Kingdom

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Folded cascade Opamp is the preferred first stage choice in a typical two-stage opamp due to relaxed headroom and better output swing. Traditional Folded cascade opamp gain and noise are compromised due to the folding transistors. In this work, FVF assisted differential pair has been proposed which extracts the signal and injects it onto the gate of the folding devices, hence they will contribute gain and noise will be reduced. A prototype was developed in 2Snm TSMC CMOS technology and post-layout simulation results were performed. The proposed opamp achieves 68dB DC gain, which is 18dB higher than the conventional one. Also, the proposal exhibits 20% improvement in the input-referred noise for the same power consumption. Further, the circuit consumes 33W of power from a 1V power supply and occupies a 78∗46m2 silicon area. © 2022 IEEE.

关键词： Operational amplifiers

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