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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Mariam Hussain Nusrat Sharmin Sumayea Binte Shafiul Dept. of Climate and Energy Systems Engineering Ewha Womans University Seoul South Korea Dept. of Computer Science and Engineering University of Science and Technology Chittagong Bangladesh Dept. of Epidemiology Bangladesh University of Health Sciences Dhaka Bangladesh

Water cycles, climate-related hazards, and agroirrigation are strongly controlled by soil moisture (SM) content. For water resource management, prediction is a key to mitigate and regulate expected economic losses and property damages. This paper compares two supervised machine learning (ML) techniques: support vector regression (SVR) and random forest (RF) to predict SM. In RStudio, various meteorological variables: temperature, relative humidity, wind speed, and rainfall are trained to estimate SM. For eight divisions, SM and weather variables are obtained from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER). The experiments include daily observations for 39 (1982 to 2020) to develop SVR and RF models. To estimate SM from the predictive model, datasets from diverse regions: Rajshahi, Mymensingh, Chittagong, and Sylhet are utilized in training (60%) and Rangpur, Barisal, Khulna, and Dhaka are segregated for validation (40%) resulting in accuracy of 88 to 95.8%. This model further is applied to forecast daily SM for each city including two districts (Bogra and Jessore) and found slightly higher model performance for SVR (90.7%) than RF (90.1%) on average (Year: 2021). For agricultural, industrial and urban water supplies as well as drought, landslides, and river erosions can be mitigated by an accurate estimation of soil moisture. The investigations encourage for providing SM budget to public with supervised ML techniques mostly among data-sparse regions.

关键词： Radio frequency Support vector machines Training Biological system modeling Soil moisture Urban areas Weather forecasting

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Enhancing Autism Disease Classification Using a Hybrid GA-KNN Approach for Feature Selection

Enhancing Autism Disease Classification Using a Hybrid GA-KN...

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International Conference on Information and Communication systems (ICICS)

作者： Maria Yousef Laith Al Shehab Doaa Abdel Ghani Hadeel Alazzam Mohammad Ghatasheh Department of Computer Science Middel East University (MEU) Amman Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Dept. of Computer Information Systems Middel East University (MEU) Amman Jordan

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

These days, autism spectrum disorder (ASD) is considered to be one of the most common health problems. A person with ASD is said to have a neurodegenerative disorder that affects their ability to engage and communicate. Using screening tests to identify autism symptoms is highly costly and time-consuming. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism symptoms early on. Early detection of autism symptoms is now feasible due to advancements in AI and ML. The main contribution of this study is to address the high dimensionality problem by proposing a new hybrid feature selection approach using the genetic algorithm (GA) with K-nearest neighbor (KNN). This hybrid approach employs the GA-KNN to identify the ideal feature subset that enhances the performance of autism classification. The model uses three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) to compare their impact on classification performance and accuracy based on the proposed approach based on three ASD datasets for adults, adolescents, and children. The experimental results show that: 1) the hybrid GA-KNN approach used in the feature selection step can reduce the total number of features from 21 to 9 and increase the classifiers accuracy by 9% to 17%; 2) out of all the tested classifiers, the CNN classifier performed the best in classification, yielding the highest accuracy of 97.5%.

关键词： Support vector machines Autism Accuracy Nearest neighbor methods Feature extraction Classification algorithms Random forests

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SLIPT in Joint Dimming Multi-LED OWC systems with Rate Splitting Multiple Access

SLIPT in Joint Dimming Multi-LED OWC Systems with Rate Split...

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IEEE International Conference on Communications (ICC)

作者： Sepideh Javadi Sajad Faramarzi Farshad Zeinali Hosein Zarini Mohammad Robat Mili Panagiotis D. Diamantoulakis Eduard Jorswieck George K. Karagiannidis Pasargad Institute for Advanced Innovative Solutions (PIAIS) Tehran Iran Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Institute for Communications Technology Technische Universitat Braunschweig Braunschweig Germany Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

关键词： Wireless communication Energy consumption Quality of service Reinforcement learning Light emitting diodes Dynamic scheduling Real-time systems

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Difference rewards policy gradients 20

Difference rewards policy gradients

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20th International Conference on Autonomous Agents and Multiagent systems, AAMAS 2021

作者： Castellini, Jacopo Devlin, Sam Oliehoek, Frans A. Savani, Rahul Dept. of Computer Science University of Liverpool Liverpool United Kingdom Microsoft Research Cambridge Cambridge United Kingdom Dept. of Intelligent Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9781713832621

Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent's contribution to the overall performance, which is crucial for learning good policies. We propose a novel algorithm called *** that explicitly tackles this by combining difference rewards with policy gradients to allow for learning decentralized policies when the reward function is known. By differencing the reward function directly, *** avoids difficulties associated with learning the Q-function as done by Counterfactual Multiagent Policy Gradients (COMA), a state-of-the-art difference rewards method. For applications where the reward function is unknown, we show the effectiveness of a version of *** that learns a reward network that is used to estimate the difference rewards. © 2021 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Reinforcement learning

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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. This paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

An Investigation of Running Load Comparisons of ARCore on Na...

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Vocational Education and Electrical Engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

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Mathematical Model of Damping of UAV Oscillations in the Cargo Delivery Problem

Mathematical Model of Damping of UAV Oscillations in the Car...

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Advanced technology (KhPIWeek), IEEE KhPI Week on

作者： Dmytro Kucherov Tetiana Shmelova Olexii Poshyvailo Valerii Tkachenko Ignat Miroshnichenko Ihor Ogirko dept. Computerized Control Systems Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine dept. Air Navigation Systems Faculty of Air Navigation Electronics and Telecommunications National Aviation University Kyiv Ukraine dept. Mathematic Ukrainian Academy of Printing City Lviv Ukraine

Currently, the use of unmanned vehicles for the delivery of goods is becoming a trend in many areas of human activity. At the same time, it is necessary to solve the problem of the integrity of the cargo because of its possible fluctuations. The paper deals with the problem of delivering cargo attached to a 4-motor UAV on a rigid cable. A control system is needed to achieve the UAV equilibrium position that can compensate for significant mismatches in the XY coordinate plane. A combination of two control algorithms is proposed for solving this problem, one of which provides a given position in the horizontal plane at small mismatch angles and the second at large mismatch angles. The first controller uses the PID controller algorithm with the corresponding optimal setting of its parameters, and the second one uses the bang-bang control scheme. An additional controller is used to coordinate these controllers, which ensures their timely switching. Some simulation is also proposed.

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A Novel Hybrid Deep Learning Model for Detecting Breast Cancer

TechRxiv

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TechRxiv 2023年

作者： Banerjee, Avishek Chakraborty, Debasis Bhattacharyya, Basanti Nath, Ira Dey, Debashis Garg, Deepak Dept. of Computer Science and Business Systems Asansol Engineering College West Bengal 713305 India Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India Dept. of Computer Science and Engineering Pailan College of Management and Technology West Bengal 700139 India Dept. of Computer Science and Engineering JIS College of Engineering West Bengal 741235 India Dept. of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal 700064 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

Cancer is a deformity of the body cells that grow out of control and spread to other parts of body. According to the American Cancer Society, early identification of cancer resulted in a 99% chance of survival in the localized stage. In this article, a benchmark cancer dataset is considered for the purpose of investigation. After data preprocessing, we attempt to predict cancer type using our proposed deep learning model named as CNN-Soft-T that combines the feature selection (FS) and classification. FS is introduced using histogram technique for anomaly detection and dimension reduction. We have compared the performance of this model to other cutting-edge machine learning approaches including Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) and Decision Tree (DT) in order to demonstrate its utility. We have experimentally exhibited the exceptional empirical effectiveness and biological applicability of our suggested hybrid model for cancer diagnostics and medication development. © 2023, CC BY.

关键词： Decision trees

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying

Talk to your data: Enhancing Business Intelligence and Inven...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Jerry Zhu Saad Ahmed Bazaz Srimonti Dutta Bhavaraju Anuraag Imran Haider Srijita Bandopadhyay Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Electronics and Communication Engineering Vellore Institute of Technology India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT’s precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape.

关键词： Industries Structured Query Language Technological innovation Costs Large language models Semantics Inventory management

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New Algorithms for the Computation of Inverse Transforms

New Algorithms for the Computation of Inverse Transforms

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Zhang, Jun Fan, Ruzong Shen, Fangyang University of Maryland Eastern Shore Department of Computer Science and Engineering Technology Princess AnneMD21853 United States Georgetown University Department of Biostatistics Bioinformatics and Biomathematics WashingtonDC20057 United States City University of New York Dept. of Computer Systems Technology New York City College of Technology BrooklynNY11201 United States

ISBN: (纸本)9798350320282

The Laplace Transform has been widely used for about two centuries in problem solving in mathematics, engineering and sciences. The Sumudu Transform is very recent, it is as powerful as the Laplace Transform and has many nice features. Traditionally, when these transforms are used, the calculations of the inverse transforms are necessary;unfortunately, the calculations of Inverse Laplace Transform and Inverse Sumudu Transform are problematic and challenging. The authors studied Sumudu Transform in the computational approaches. We shall introduce a few novel algorithms on the computations of Inverse Sumudu Transform and Inverse Laplace Transform in this paper. The algorithms introduced here are straightforward to understand and powerful for problem solving, and some demo versions were implemented in the Maple computer Algebra systems. Type: Full / Regular Research Paper for CSCI-RTCS © 2022 IEEE.

关键词： Laplace transforms

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