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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP International Federation for Information Processing 2025.

关键词： Clinical research

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Adaptive Numerology-based Rate Optimization for enhanced-Vehicle-to-Everything Use Cases in Beyond 5G Networks 17

Adaptive Numerology-based Rate Optimization for enhanced-Veh...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Hasan, Nadine Kumar, Abhinav Kataoka, Kotaro Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331531195

The existing 5G new radio (NR) numerology supports certain values of parameters like subcarrier spacing, symbol duration, and guard interval for vehicle-to-everything (V2X) communications. However, with ever evolving use cases for enhanced-V2X in beyond 5G networks, these recommended values are not sufficient to provide the required quality of service (QoS). Motivated by this, we propose an adaptive numerology scheme (ANS) that dynamically selects these parameters to mitigate the effect of Doppler and multi-path. We present exhaustive simulation results demonstrating the efficiency of the proposed scheme for the dynamic nature of V2X communication networks. © 2025 IEEE.

关键词： Vehicle to Everything

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Double-Sided Integrated GaN Power Module with Double Pulse Test (DPT) Verification

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Journal of Microelectronics and Electronic Packaging 2023年第2期20卷 71-81页

作者： Sinha, Sourish S. Cheng, Tzu-Hsuan Hopkins, Douglas C. Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States

Wide Bandgap devices (WBG) have led to an era of high-speed and high-voltage operations that were not previously achievable with silicon devices. However, packaging these devices in the power module has been a challenge due to higher switching rates, which can cause several amperes of displacement current to flow through the parasitic capacitance of the package, thus impacting the gate driver operation and the switching ability of the device. The severity of this current increases in thin packaging substrates, unlike the traditional inorganic substrates, e.g., Direct Bond Copper (DBC) and thus, a thorough investigation is needed before it can be used with WBG semiconductors. The objective of this article is to discuss ways to reduce as well as manipulate the parasitic capacitance at different locations in the power modules to reduce the magnitude of the peak and Root Mean Square (RMS ) value of the displacement current and have a better gate drive signal and power waveform. To study this, a Double Pulse Test (DPT) simulation study has been conducted to show how an intelligent distribution of parasitic capacitance benefits the device functioning. This has been validated through experimental fabrication and DPT of dense power module following proposed guidelines. A detailed description of the design of a high-speed capable DPT circuit and measurement setup has been specified to show the steps needed for reliable testing and measurement. © 2023 International Microelectronics Assembly and Packaging Society.

关键词： Gallium nitride

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Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 42-51页

作者： Badana Mahesh Mandava Kranthi Kiran Department of Computer Science and Engineering GITAM Deemed to be UniversityVishakhapatnam 561203India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology&SciencesVishakhapatnam 531162India

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk *** estimation techniques often struggle to provide accurate estimates due to the complex nature of software *** recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation *** study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort *** proposed hybrid model takes advantage of the strengths of both LSTM and RF *** evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental *** experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and *** integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development *** hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

关键词： software effort estimation hybrid model ensemble learning LSTM temporal dependencies non⁃linear relationships

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A CNN-based framework for land use land cover classification of heterogeneous terrain using satellite images

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Neural Computing and Applications 2025年 1-28页

作者： Tarafdar, Anurina Middya, Asif Iqbal Banerjee, Sounak Khatua, Sunirmal Roy, Sarbani Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Anandapur East Kolkata Township West Bengal Kolkata700107 India Department of Computer Science and Engineering Jadavpur University 188 Raja S.C. Mallick Rd West Bengal Kolkata700032 India Department of Computer Science and Engineering University of Calcutta JD-2 JD Block Sector III West Bengal Bidhannagar Kolkata700106 India

One of the important topics in remote sensing is land use land cover classification. This paper presents a framework that aims to correctly classify land use land cover into seven different landscape characteristics such as agricultural area, agricultural green area, greenery, land, mountain region, settlement, and water body using a convolutional neural network (CNN) model. Thousands of satellite images of different regions in India are obtained from the Google Maps platform using the Maps Static API. These images are pre-processed using image augmentation techniques, and the proposed CNN model is thoroughly trained with this dataset. Several existing deep CNN models such as VGG-16, ResNet-50, and InceptionV3 are also considered. Instead of training these CNN models from scratch, transfer learning is used and these pre-trained networks are fine-tuned by replacing the final layers with additional layers. All the models are trained rigorously using the satellite image dataset and their performances are compared. Results show that the proposed CNN model classifies the test dataset with 89.03% accuracy. Although ResNet-50 provides the most promising results with a classification accuracy of 90.94% on the test dataset, the proposed CNN model has much fewer parameters than ResNet-50, and therefore its training and prediction times are shorter. The model training time and the per-image prediction time of the proposed model are 77.06% and 30.55% less than those of ResNet-50, respectively. Some case studies are also conducted to show the application of the proposed model in classifying the land use land cover of different locations. Additionally, the efficacy of the model is validated by using it for land use land cover classification on the RGB version of the benchmark EuroSAT dataset. The results show that the proposed model can effectively perform land use land cover classification on the EuroSAT dataset with 94.02% accuracy. © The Author(s), under exclusive licence to S

关键词： Convolutional neural networks

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Supervised Learning-Driven Emotion Recognition through Facial Landmark Trajectories Enhanced by SMOTE Methodology

Supervised Learning-Driven Emotion Recognition through Facia...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Kar, Ishita Chatterjee, Mehuli Manna, Sukanya Das, Akash Dutta, Stobak Department of Computer Science and Engineering St Thomas’ College of Engineering and Technology Kolkata India

ISBN: (纸本)9798331536336

The study highlights the importance of facial landmark trajectories in understanding mental health by investigating the application of supervised learning systems to identify emotions. Diagnoses and treatment plans are impacted by the difficulty in recognizing and controlling emotions, which are intrinsically tied to mental health. Although physiological signals like ECG and EEG are accurate, they have drawbacks including expense and user discomfort. Because they are noninvasive, facial landmarks present a viable substitute for practical uses. Using information from the ASCERTAIN dataset, this study divided emotions into four valence-arousal plane quadrants: High Arousal High Valence (HAHV), High Arousal Low Valence (HALV), Low Arousal High Valence (LAHV), and Low Arousal Low Valence (LALV). Algorithms including Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM) were used to solve a 4-class classification problem. A common problem, class imbalance, was lessened by employing the Synthetic Minority Oversampling Technique (SMOTE). The study showed that SMOTE greatly enhanced predictions for minority classes, particularly for LALV, whereas classifiers strongly preferred the majority class in the absence of balance. Despite possible overfitting, Decision Tree has the greatest true positive rate among classifiers for LALV after SMOTE application. The findings demonstrate developments in emotional computing and provide information for applications in mental health support and human-computer interaction. © 2025 IEEE.

关键词： k-nearest neighbors

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EDUVERSE: Inclusive and Immersive Metaverse Learning Environment for Education

EDUVERSE: Inclusive and Immersive Metaverse Learning Environ...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Madhubala, R. Gomathi, V. Jothikumar, Karthikeyan National Engineering College Department of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331529833

Eduverse is an advanced Virtual Reality (VR) platform developed to transform traditional education by creating interactive and immersive virtual learning environments. Designed to support both students and teachers, Eduverse simulates real-world classrooms, discussion rooms, and conference halls, enabling real-time communication and collaboration. One of its standout features is the accessibility tools, such as text-to-speech (TTS) and speech-to-text (STT) systems, which make the platform highly beneficial for students with disabilities. The TTS system helps students with speech impairments by converting their typed messages into spoken words, while the STT system transcribes spoken communication into text for students who have difficulty *** addition to accessibility, Eduverse offers customizable avatars, allowing students to represent themselves digitally, which helps reduce social anxiety and encourages participation. The platform also provides teachers with speech management tools, enabling them to moderate discussions, assign speaking privileges, and manage class activities. This ensures that every student has the opportunity to contribute, even in larger classes. Eduverse has been tested within a set of students, showing significant improvements in student engagement, participation, and inclusivity compared to traditional online learning platforms. Students with disabilities reported that the integrated accessibility tools allowed them to engage more fully in class discussions, making Eduverse a valuable solution for inclusive education. By combining a collaborative and supportive learning space, Eduverse demonstrates the potential to improve the way students learn and interact in the virtual world. © 2025 IEEE.

关键词： Students

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Electric Vehicle Charging System Controller Analysis and Synthesis

Electric Vehicle Charging System Controller Analysis and Syn...

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2025 IEEE Green Technologies Conference, GreenTech 2025

作者： Perez, Agustin Omar Yilmaz, Muhittin Texas A&M University-Kingsville Electrical Engineering Computer Science Department KingsvilleTX United States

ISBN: (纸本)9798331520182

This research investigates a type of converter and charging system in terms of closed-loop design for a potentially superior charging system for electric vehicle, resulting in better battery life and energy efficiency. A recent rectifier-charging system is analyzed in terms of the controller and given controller types. A baseline with the existing design was implemented and different controller combinations, as part of the proportional-integral-derivative controller, were investigated about their effects on the charging circuit operations and related battery performance levels. Simulation results imply that different controllers result in different output characteristics, and potential controller optimizations, in addition to the circuit operations, are likely to yield superior charging operations. © 2025 IEEE.

关键词： Two term control systems

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PsychoLex: Unveiling the Psychological Mind of Large Language Models 1

PsychoLex: Unveiling the Psychological Mind of Large Languag...

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1st Workshop on NLP for science, NLP4science 2024

作者： Abbasi, Mohammad Amin Mirnezami, Farnaz Sadat Naderi, Hassan Department of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (纸本)9798891761858

This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex1 , a suite of resources designed to enhance LLMs' proficiency in psychological tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice. © 2024 Association for Computational Linguistics.

关键词： Psychoacoustic

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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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