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Investigation of Teachers' Use of Information Technology in the Enhanced Learning-Teaching Process of Tenth-Grade Students : (Case Study: Shahed Nadjmieh Girls' Vocational School)

Investigation of Teachers' Use of Information Technology in ...

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International Conference on E-Learning and E-Teaching (ICELET)

作者： Mehrshad Khosraviani Zahra Shams Mohammadreza Abbassian Dept. of Computer Engineering Islamic Azad University - Parand Branch Tehran Iran Dept. of IT Engineering Mehralborz University Tehran Iran Dept. of IT Engineering Mehralborz University Qom Iran Dept. of Media Management University of Tehran Tehran Iran

ISBN: (数字)9798331599416

ISBN: (纸本)9798331599423

The research aimed to evaluate the gap between the current and desired conditions of IT-related elements that influence the teaching and learning process within a vocational school environment. A comparative research design was utilized, featuring a questionnaire created by the researcher, which was based on a five-point Likert scale for the purpose of data collection. The study involved tenth-grade students and teachers, with a sample size of 60 participants chosen through non-random selection. The questionnaire not only gathered demographic details but also included 20 closed-ended questions to evaluate the extent of technology usage. The validity of the questionnaire was affirmed by nine experts, while its reliability was determined to be 0.786. Data analysis was conducted using descriptive statistics, frequency tables, and T-tests. The results indicated a positive correlation between teachers' knowledge, attitudes, and skills in relation to technology usage. In conclusion, the study found that the level of technology use among teachers falls short of the optimal status and that the integration of technological tools significantly enhances student learning.

关键词： Training Resistance Technological innovation Open Educational Resources Strategic planning Information and communication technology Teamwork Reliability Problem-solving Creativity

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Adaptive Output Regulation of MIMO LTI Systems with Unmodeled Input Dynamics

Adaptive Output Regulation of MIMO LTI Systems with Unmodele...

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IEEE Conference on Decision and control

作者： Oleg Borisov Alberto Isidori Anton Pyrkin Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia Dept. of Computer Control and Management Engineering University “Sapienza” Rome Italy

This paper addresses the problem of adaptive output regulation of a minimum-phase MIMO LTI system in the presence of un-modeled (fast) input dynamics. The adoption of a post-processing tunable internal model makes it possible to implement standard methods for the analysis of two-time-scale systems. The proposed adaptation law guarantees, under suitable hypotheses, convergence to zero of the regulation error as well as of the parameter estimation error.

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Enhancing Automatic Evaluation Systems for Descriptive Answers Using BERT: Addressing Context and Coherence Challenges

Enhancing Automatic Evaluation Systems for Descriptive Answe...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Sagarika Dash Dhawaleswara Rao CH Rudra Kalyan Nayak M.V.B.T. Santhi Dept. of Computer Science Engineering Centurion Univrsity of Technology and Management Odisha India Dept. of Computer Science Engineering VIT Bhopal Univrsity Sehore Madhya Pradesh India Dept. of Computer Science Engineering Koneru Lakshmalah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This study explores the application of advanced Natural Language Processing (NLP) techniques, specifically leveraging BERT-based models, for evaluating semantic similarity between text inputs. The methodology involves preprocessing text through tokenization, stemming, and sentence chunking, followed by the generation of sentence embeddings using the ‘bert-base-nli-mean-tokens’ model. The model, fine-tuned on Natural Language Inference (NLI) tasks, captures deep contextual relationships, enhancing the ability to compute text similarity through cosine similarity of high-dimensional embeddings. The evaluation used datasets from Semantic Textual Similarity (STS) benchmarks, combined with educational text samples. Key metrics such as Pearson correlation, Spearman correlation, and Root Mean Squared Error (RMSE) were employed to assess the model's performance in predicting similarity scores between text pairs. Results demonstrate that the model achieves a Pearson correlation of 0.53, indicating moderate agreement with human-annotated scores, while the Spearman correlation score of 0.26 highlights a weaker monotonic relationship. An RMSE of 0.12 further suggests low prediction error, supporting the model's efficacy in identifying similarities between text pairs. These findings underscore the potential of BERT-based models in applications like automated essay grading, paraphrase detection, and content-based recommendation systems, where accurate assessment of text similarity is critical

关键词： Measurement Correlation Accuracy Computational modeling Semantics Predictive models Signal processing Transformers Tokenization Context modeling

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Real-Time and Low-Overhead Graph Task Scheduling over Vehicular Computing-Assisted Edge Networks

Real-Time and Low-Overhead Graph Task Scheduling over Vehicu...

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IEEE International Conference on Communications (ICC)

作者： Bingshuo Guo Minghui Liwang Fan Yang Seyyedali Hosseinalipour Xianbin Wang Huaiyu Dai Dept. of Information and Communication Engineering Xiamen University Xiamen China Dept. of Control Science and Engineering Tongji University Shanghai China Dept. of Electrical Engineering University at Buffalo-SUNY NY USA Dept. of Electrical and Computer Engineering Western University Ontario Canada Dept. of Electrical and Computer Engineering North Carolina State University Raleigh USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.

关键词： Energy consumption Processor scheduling Network topology Merging computer architecture Real-time systems Topology

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Ensemble-Based Explainable Approach for Rare Medicinal Plant Recognition and Conservation

Ensemble-Based Explainable Approach for Rare Medicinal Plant...

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Information and Network Technologies (ICINT), International Conference on

作者： Shakil Khan Hamdadur Rahman Md. Ismail Hossain Siddiqui Zishad Hossain Limon Mahbub Alam Khan Rezaul Haque Md. Redwan Ahmed Ahmed Wasif Reza Shamim Hasnat Ripon Dept. of Management of Information System International American University CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

Accurate classification of medicinal plants, particularly those that are threatened, rare, or endangered, is crucial for biodiversity conservation and sustainable use. This study introduces a robust ensemble-based classification model that integrates various architectures, including EfficientNet-B7, ConvNeXt, NasNet, and XceptionNet. Data augmentation with normalization enabled a training process using 4,500 high-resolution leaf images from a balanced dataset while helping achieve improved performance metrics. Transparent insight into model decisions became possible through our integration of Explainable AI (XAI) techniques which provided interpretable information about how the model reached its output. This transparency enables researchers and conservationists to better understand the system’s outputs. The ensemble model achieved an impressive accuracy of 99.29%, surpassing the performance of individual models and demonstrating strong generalization capabilities. We also built a real-time web application that delivers useful biodiversity monitoring solutions and helps assess species for pharmaceutical research and conservation initiatives. The study demonstrates AI-driven potentials through its analysis but it reveals two main challenges including dataset scope boundaries and high computational costs of modeling. Developments in future research will concentrate on enhancing database size along with computational strategies to enable mobile applications as field identification tools. This research has developed a flexible and dependable system which unites artificial intelligence investigation with practical environmental protection necessities.

关键词： Medicinal plants Accuracy Biological system modeling Computational modeling Real-time systems Mobile applications Biodiversity Environmental management Monitoring Pharmaceuticals

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Using Pyramid Vision Transformer for Assisting Glaucoma Diagnosis with Small Datasets 21

Using Pyramid Vision Transformer for Assisting Glaucoma Diag...

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21st International Conference on Networking, Sensing and control, ICNSC 2024

作者： Wu, Qinhao Yao, Shuaiyu Ouyang, Wendi Huang, Jingzhe Kang, Qi Eindhoven University of Technology Dept. of Mathematics and Computer Science Eindhoven Netherlands Coll. of Electronic and Information Engineering Tongji University Shanghai China Coll. of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing China King's College London Dept. of Mathematics London United Kingdom

ISBN: (纸本)9798350365221

Glaucoma is one of the leading causes of blindness worldwide. Patients with open-angle glaucoma have difficulty realizing the graduate vision loss. However, the optic nerve head's morphological change can manifest in the retinal image. Therefore, many studies offer computer-aided glaucoma diagnosis based on retinal image analysis. With the trend of deep learning, there have been several attempts to utilise deep-learning-based glaucoma classification in practice. However, the limited size of the dataset is a problem when adapting deep-learning models to glaucoma classification. Therefore, we test three vision transformer models to handle this problem and demonstrate their performance in glaucoma classification. Also, based on their performance, we give suggestions for their usage in clinical practice. In this paper, we fine-tuned three transformer models for glaucoma classification using two datasets, RIM-ONE-DL and Drishti-GS. Based on our experiments, the pyramid vision transformer (PVT) model achieves the best performance, with good scores in terms of accuracy and sensitivity. This suggests that the PVT can catch the missing detail and can be used to support the glaucoma diagnosis. © 2024 IEEE.

关键词： Ophthalmology

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Semantic Communications Based on Adaptive Generative Models and Information Bottleneck

arXiv

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arXiv 2023年

作者： Barbarossa, Sergio Comminiello, Danilo Grassucci, Eleonora Pezone, Francesco Sardellitti, Stefania Di Lorenzo, Paolo Department of Computer Control and Management Engineering Sapienza University of Rome Italy The Dept. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Italy

Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In semantic communications, the scope of the destination is not to recover a list of symbols symbolically identical to the transmitted ones, but rather to recover a message that is semantically equivalent to the semantic message emitted by the source. This paradigm shift introduces many degrees of freedom to the encoding and decoding rules that can be exploited to make the design of communication systems much more efficient. In this paper, we present an approach to semantic communication building on three fundamental ideas: 1) represent data over a topological space as a formal way to capture semantics, as expressed through relations;2) use the information bottleneck principle as a way to identify relevant information and adapt the information bottleneck online, as a function of the wireless channel state, in order to strike an optimal trade-off between transmit power, reconstruction accuracy and delay;3) exploit probabilistic generative models as a general tool to adapt the transmission rate to the wireless channel state and make possible the regeneration of the transmitted images or run classification tasks at the receiver side. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Comprehensive Review of the Application of Greenhouse Using the Internet of Things 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kumar, Santosh School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Lavale Campus Pune India Department of Management Jaipuria Institute of Management Jaipur India CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

The primary objective of researching the Internet of Things as a tool is to regulate and oversee agriculture in order to facilitate the growth of a greater variety of crops and an increase in the quantity of crops produced overall. Farmers are subjected to a wide range of challenges, including uneven distribution of water, unstable weather conditions (such as wind, hailstorm, UV radiations, and pesticide assaults), and a diverse range of soil types. Because water should be spread in an equal manner, it becomes extremely difficult to control the uneven distribution of water;hence, a new irrigation system has to be created in order to circumvent this challenge. Greenhouse has shown to be a very helpful tool in resolving all of these issues. An inclined technology is one that utilizes the Internet of Things in order to make greenhouse a more adaptable platform. In this case, a GSM module is used for the purpose of managing the tube well, in addition to a fogger and a variety of sensors for regulating the temperature and the relative humidity. Large regions are now covered by sensor networks, which is helpful since it allows for the creation of greenhouses with realistic environments, which plays a significant role in the development of crops. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensor networks

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UAV Platform for Real-time Indoor Mapping

UAV Platform for Real-time Indoor Mapping

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Spruha Nitin Kshirsagar Abhishek Rawat Ravi Bhandari Dept. of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India Dept. of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research and Management (IITRAM) Ahmedabad India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

SLAM enables Robots and UAVs to effectively understand unknown surroundings and estimate their own position within those environments. Recent advancements in 2D mapping have significantly increased its popularity for navigation tasks, primarily because it is easier to generate and requires less computational power compared to 3D mapping. This paper provides a comparison of two widely used ROS-based 2D SLAM libraries: Google Cartographer and Hector SLAM, deployed on a custom built UAV that uses 2D LiDAR, and finds Hector SLAM to be more feature rich as well as computationally efficient.

关键词： Cartography Technological innovation Simultaneous localization and mapping Laser radar Three-dimensional displays Signal processing Autonomous aerial vehicles Real-time systems Internet Robots

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