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An Advanced MIMO Multicarrier Framework for NTN-Based 5G Broadcast/Multicast Transmissions 11

An Advanced MIMO Multicarrier Framework for NTN-Based 5G Bro...

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11th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2024

作者： Rahman, Talha Faizur Marojevic, Vuk Sacchi, Claudio Mississippi State University Dept. of Electrical and Computer Engineering Mississippi StateMS United States University of Trento Dept. of Information Engineering and Computer Science Trento Italy The University of New Mexico Dept. of Electrical Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350385045

Satellites are teaming up with 5G, forming a non-terrestrial network (NTN), to support broadband applications over wide coverage areas. However, low latency and high reliability are the challenges in NTN which is addressed through the use of low-earth orbit (LEO) satellites. High transmission efficiency can be accomplished by employing multiple-input multiple-output (MIMO) antenna systems and multicarrier wave-forms. Conventional multicarrier techniques, such as Orthogonal Frequency Division Multiplexing (OFDM) and its variants, are sensitive to the nonlinear distortion of high power amplifiers (HPAs) and high Doppler environments. Therefore, we consider Constant Envelope OFDM (CE-OFDM) assisted with Space-Time Shift Keying (STSK) and Low-Density Parity Check (LDPC) encoding for 5G broadcast/multicast systems based on LEO satellites working in the L-band. We propose a low complexity receiver based on A Posteriori Probability (APP) STSK decoding integrated with LDPC decoding, in which we derive the APP of LDPC coded bits for the STSK-CE-OFDM transmission. We analyze the computational complexity of the proposed receiver scheme and conclude that it is effective in counteracting HPA nonlinearity and high Doppler effects inherent to LEO satellites. Numerical results show that the proposed framework outper-forms conventional multicarrier techniques with a gain as high as 16 dB in the presence of amplifier nonlinearity. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few Machine Learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector Machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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Tracking Dynamic Conditional Neural Correlation during Task Learning 46

Tracking Dynamic Conditional Neural Correlation during Task ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Zixu Chen, Shuhang Li, Mingdong Wang, Yiwen The Hong Kong University of Science and Technology Dept. Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350371499

Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time for better performance. To investigate how neurons adjust their firing patterns during learning, it's essential to track the time-variant correlation. In this paper, we propose to mathematically model the dynamical CNC by implementing the integrated point process filter which incorporates neural correlation and single neural tuning into decoding. Specifically, we generate synthetic M1 neurons' firing data to simulate the dynamic change of the conditional neural correlation over days, while a rat learns a two-lever discrimination task. By comparing the tracked CNC with the designed CNC, our results show that the CNC can be better tracked over time by CIPPF than that of decoder assuming conditional independence among neurons, which indicates the possibility to better understand the brain dynamics during task learning. © 2024 IEEE.

关键词： Decoding

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Test Suite Minimization Through PSO 11

Test Suite Minimization Through PSO

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ahuja, Neeru Bhatia, Pradeep Kumar Guru Jambheshwar University of Science and Technology Dept. of Computer Science and Engineering Haryana Hisar125001 India

ISBN: (纸本)9798350350357

Regression testing is a critical stage that ensures the software changes are not affecting existing functionality. Ideally, conducting regression tests after each software change is essential. However, the exhaustive repetition of full tests for every alteration proves time-consuming, expensive, and demands significant resources. Test suite minimization serves as a solution by eliminating redundant test cases. This research presents an advanced approach to eliminate redundant test cases effectively. The proposed method focuses on identifying a concise yet effective set of test cases that align with requirements and efficiently uncover faults. This streamlined approach not only reduces testing expenses but also contributes to a more efficient testing process. The study demonstrates its effectiveness in minimizing the test suite by employing the Particle Swarm Optimization (PSO) algorithm with varied inertia weight strategies. Results from simulations indicate the success of this approach in reducing the test suite size while maintaining or even improving code coverage. Comparative analysis against Genetic Algorithms (GA) and Artificial Bee Colony (ABC) methods underscores the superior performance of PSO, specifically with tailored inertia weight strategies. This research significantly contributes to optimizing software testing methodologies, ultimately enhancing fault detection efficiency in software development. © 2024 IEEE.

关键词： Software testing

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IoT-Driven Precision Crop Recommendation System Using LSTM & TSC-NET for Sustainable Agriculture 1

IoT-Driven Precision Crop Recommendation System Using LSTM &...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Satya Sandeep, K. Lakshmi Sahithi, K. Shabana Azmi, Sd. Renuka Devi, J. Nirmala Jyothi, B. Vignan 'S Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384246

Andhra Pradesh is a well-known agricultural state with a major contribution to the production of rice, cotton, and chilies it is also known as the 'Rice Bowl of India'. Unpredictable weather events have been a concern to the state, it is proving to show a negative impact on the state's farming population, nevertheless the state still contributes materially to the country's cultivation production. on thorough research and historical analysis, it is stated that the food diffidence and conservationist problems arise from the established age-old agricultural methods, these methods are being followed without any systematic scientific pursuit. this study pursues to solve the mentioned problem by transitioning agricultural practices with the use of Internet of Things (IoT) technologies. A preferred multi-class classification model leveraging IoT data. the data primarily includes meteorological parameters such as soil NPK values, temperature, humidity, and more. The hypothesized model, a hybrid of Long Short-Term Memory (LSTM) and Time Series - Convolutional Neural Networks (TSC-NET), which recommends suitable crops for the cropland location, it is accomplished by integrating Time-space data. the food grain acreage in Andhra Pradesh is seen dwindling by 4.2 % in 2022-2023, underscoring the susceptibility of farmers to monsoon failures. In addition, macroeconomically speaking, the slowdown in agrarian development disrupts economy's advancement as a whole, advocating for agrarian development. The IoT -driven crop classification model provides the novel approach to enhance crop selection and optimize production potential. therefore, improving the precision and efficacy of crop forecasting while enabling farmers to make well-informed decisions accounting for soil conditions and climate data. It intends to tackle both macro-level economic concerns and micro-level farmer vulnerabilities, this research levels to improve the sustainability and efficiency of agriculture in Andhra Pr

关键词： Internet of things

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Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets 41

Beyond the ROC Curve: Classification Trees Using Cost-Optima...

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41st International Conference on Machine Learning, ICML 2024

作者： Gabidolla, Magzhan Zharmagambetov, Arman Carreira-Perpiñán, Miguel Á. Dept. of Computer Science and Engineering University of California Merced United States United States

Important applications such as fraud or spam detection or churn prediction involve binary classification problems where the datasets are imbalanced and the cost of false positives greatly differs from the cost of false negatives. We focus on classification trees, in particular oblique trees, which subsume both the traditional axis-aligned trees and logistic regression, but are more accurate than both while providing interpretable models. Rather than using ROC curves, we advocate a loss based on minimizing the false negatives subject to a maximum false positive rate, which we prove to be equivalent to minimizing a weighted 0/1 loss. This yields a curve of classifiers that provably dominates the ROC curve, but is hard to optimize due to the 0/1 loss. We give the first algorithm that can iteratively update the tree parameters globally so that the weighted 0/1 loss decreases monotonically. Experiments on various datasets with class imbalance or class costs show this indeed dominates ROC-based classifiers and significantly improves over previous approaches to learn trees based on weighted purity criteria or over- or undersampling. Copyright 2024 by the author(s)

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Software Effort Estimation using ANN (Back Propagation) 7

Software Effort Estimation using ANN (Back Propagation)

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ravi Kumar, B.N. Suresh, Yeresime Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India Dept. of Computer Science and Engineering Ballari Institute of Technology and Management Ballari India

ISBN: (纸本)9781665464086

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results. © 2023 IEEE.

关键词： Neural networks

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks 15

Enhanced Predictive Modeling Techniques for Early Detection ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jha, Sneha Sahu, Prakash Kumar, Santosh CSVTU Dept. of Data Science Chhattisgarh India IIIT-Naya Computer Science & Engineering Raipur India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of 9 4% and a validation accuracy of 9 0%. In comparison, the RMSprop optimizer yielded a training accuracy of 9 2% and a validation accuracy of 8 8%. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Movie Genre Prediction Using AI-Generated Data and Natural Language Processing 26

Movie Genre Prediction Using AI-Generated Data and Natural L...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Abontee, Ayesha Parpia Rayhan, Md. Abu Rahman, Md. Fuadur Sultana, Tasnim Ahmed, Ajwa Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Brac University Dept. of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350359015

Every movie belongs to one or more specific genre. In this work, a dataset was prepared consisting of AI-generated movie titles and plots of 10 different genres. Text vectorization was used to convert every single word of the titles and plots into vectors. These vectors were used to predict which genre each movie belongs to. For training, a sequential neural network model was built and used, and an accuracy of almost 80% was achieved. © 2023 IEEE.

关键词： Embedding GPT-3.5 Neural Network NLP Text Vectorization

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