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The impact of entrepreneurship education on entrepreneurship intention in the engineering studies

Procedia Computer science 2023年 225卷 3919-3928页

作者： Karin Kurata Kodama Kota Takashi Morinaga Hiromasa Ohnishi Sou Takahashi Jun Sato Kuniaki Yajima Dept. of Creative Engineering National Institute of Technology Tsuruoka College Tsuruoka 997-8511 Japan School of Data Science Nagoya City University Nagoya 467-8501 Japan Dept. of Creative Engineering National Institute of Technology Sendai College Sendai 989-3128 Japan

Entrepreneurship education is characterized by interactive learning linked to business and community initiatives. In entrepreneurship course, activity sessions include (1) Causation, and effectuation (2) Devil's River, Valley of Death, and Darwin's Sea, (3) Case Studies, (4) Guest Speaker Sessions, (5) Lean Startup, Business Model Canvas (BMC), Value Proposition Canvas (VPC), etc. are often addressed. The goal of entrepreneurship education is to change students' mindsets in terms of how they view innovative and risk-taking activities in business. There are several ways to measure the change derived from entrepreneurship education, including affective, cognitive, and skill-based outcomes. A previous study examined the effect of entrepreneurship education on entrepreneurship in relation to three independent variables: perceived behavioral control (PBC), attitude (AT), and subjective norm (SN). Combined with the results of the Generic Skill measure, this study proposes an evaluation method for entrepreneurship education that focuses on entrepreneurship intention.

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IsoGAN: A Gan Based Method for Isometric View Images Generation From Three Orthographic Views Contour Drawings

IsoGAN: A Gan Based Method for Isometric View Images Generat...

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Thao Nguyen Phuong Hidetomo Sakaino Vinh Nguyen Duy Data Solution Dept. (FCJ.ABC) FPT Consulting Japan AI-Image Group FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from three orthographic views line drawings using deep learning remains unresolved. Existing image-to-image translation techniques often generate images from just one input image. In this paper, we propose a novel method for the above task using a GAN-based model, namely IsoGAN. This method takes three images of object’s front, side, and top view as input, then analyzes the spatial and geometrical relations between each view and finally generates the corresponding isometric view image of the object. Extensive experiments on SPARE3D dataset show promising results of IsoGAN on isometric view generation task, demonstrating the effectiveness of the proposed IsoGAN.

关键词： Deep learning Solid modeling Design automation Three-dimensional displays Image synthesis Shape Transforms Vectors Generators Image reconstruction

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Actively Adaptive Multi-Armed Bandit Based Beam Tracking for mmWave MIMO Systems

Actively Adaptive Multi-Armed Bandit Based Beam Tracking for...

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IEEE Conference on Wireless Communications and Networking

作者： Ashim Kumar Arghyadip Roy Ratnajit Bhattacharjee Dept. of Electronics and Electrical Engineering Indian Institute of Technology Guwahati India Mehta Family School of Data Science and Artificial Intelligence (MFSDSAI) Indian Institute of Technology Guwahati India Dept. of Electronics and Electrical Engineering & MFSDSAI Indian Institute of Technology Guwahati India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Beam tracking methods are instrumental for efficient use of the multi-gigahertz bandwidth available at mmWave frequencies. In this paper, we propose a Multi-armed Bandit (MAB) based Reinforcement Learning (RL) algorithm to periodically select transmitter-receiver beam pairs so as to maximize the average spectral efficiency. Contrary to a traditional Bayesian MAB-based approach, the MAB algorithm proposed by us can track a user as it moves across multiple correlation distances. The algorithm keeps track of the received signal strength to detect a change in the channel correlation and adjusts its strategy to adapt to the new channel conditions. We derive an upper bound on the regret of the proposed algorithm. The proposed algorithm is evaluated on channel data generated using the open-source simulator NYUSIM and is observed to outperform existing algorithms, thus removing the requirement of repeated initial access procedures.

关键词： Correlation Upper bound Protocols Tracking Change detection algorithms Spectral efficiency Instruments

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Question Categorizer: An Automated Approach For Organizing Queries

Question Categorizer: An Automated Approach For Organizing Q...

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Networking and Communications (ICNWC), International Conference on

作者： Naina Roy Khushi Upadhyay Payal Doshi Tatwadarshi P. Nagarhalli Dept. of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India Prime SofTech Solutions Pvt. Ltd. Pune India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Question categorization is an essential task in natural language processing (NLP) and information retrieval which is the process of automatically assigning a category or class label to a given question based on its content. Categorizing questions plays a pivotal role in enhancing comprehension and facilitating targeted responses. This paper delves into the development and implementation of an advanced question categorization system which classifies questions into eight distinct categories: Analytical, definition, Theoretical, Inferential, Formulation, Differential, Mathematical and Statistical thereby encompassing a wide spectrum of question types. The foundation of this system lies in the integration of a sophisticated BERT architecture, natural language processing techniques, and domain-specific expertise. The system autonomously processes input queries, identifying the underlying characteristics that define each category and assigns the category to the input data. The model is trained on a curated set of data and the proposed methodology achieved promising results with an accuracy score of 0.98 for the 8 categories multiclass classification.

关键词： Training Transformers Information retrieval Natural language processing data models Robustness Filling

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large Vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and Vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing

DeepCare: Deep Learning-Based Smart Healthcare Framework usi...

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2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed Univ. Dept. of Electronics and Communication Engineering Tamil Nadu Thanjavur India School of Applied Computational Sciences MMC Dept. of Data Science and Computer Science United States Indian Institute of Technology Dept. of Electronics and Electrical Comm. Engg. Kharagpur India

ISBN: (纸本)9781665473408

5G and beyond communication networks require satisfying very low latency standards, high reliability, high-speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecommunication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software-Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability. © 2022 IEEE.

关键词： Deep learning

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Exploring the Impact of IoT data Diversity on Machine Learning Model

Exploring the Impact of IoT Data Diversity on Machine Learni...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Dhananjay Bisen Nookala Venu Aditya Dubey Priyanka Garg Suresh Salendra T. Lakshmi Devi Center for Internet of Things Madhav Institute of Technology & Science Gwalior M.P India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior M.P India Dept. of Compter Science and Engineering Balaji Institute of Technology & Science Warangal Telangana India Dept.of CSE-Data Science Siddhartha Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The past few years have witnessed a remarkable change by the rapid expansion of the Internet of Things (IoT). The increasing spread of networked devices in various areas of the Internet of Things has resulted in a huge amount of machine data. This data contains rich information that can be harnessed across domains such as healthcare, agriculture, smart cities, and manufacturing. To effectively analyze and capitalize this information, the integration of machine learning (ML) methods is essential. Thus, well-known machine learning techniques including Logistic Regression, Decision Tree, KNN, SVM, Random Forest, and Naive Bayes are used in paper. The performance of the algorithms is analyzed using three diverse IoT datasets and compared to one another using pre-defined measures such as accuracy, precision, recall, F1-score and execution time. Furthermore, test findings shown that the random forest algorithm consistently outperformed other algorithms for a variety of datasets with up to 98% accuracy.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Real-time systems Bayes methods Internet of Things Decision trees Random forests

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset 11

Comparative Analysis of Machine Learning Techniques for Arab...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Attar, Hani Sharrab, Yousef Smadi, Saja Jbaily, Safa Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Isra University Department of Data Science and Artificial Intelligence Amman Jordan Yarmouk University Dept. Computer Science Irbid Jordan

ISBN: (纸本)9798331509293

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency;indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications. © 2025 IEEE.

关键词： Logistic regression

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Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

arXiv

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arXiv 2025年

作者： Taha, Ahmed M. Aly, Salah A. Darwish, Mohamed F. Dept. of CE Faculty of Engineering Egypt University of Informatics Cairo Egypt Faculty of Computing and Data Science Badya University Giza Egypt Faculty of Science Fayoum University CS&Math Branch Fayoum Egypt Dept. of Pathology Faculty of Medicine Badya University Giza Egypt

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuraies;and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics. © 2025, CC0.

关键词： Magnetic resonance imaging

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things Statistics RFID tags Monitoring Accidents

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