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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

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computers, Materials & Continua 2022年第3期70卷 5305-5319页

作者： Talha Mahboob Alam Kamran Shaukat Adel Khelifi Wasim Ahmad Khan Hafiz Muhammad Ehtisham Raza Muhammad Idrees Suhuai Luo Ibrahim A.Hameed Department of Computer Science and Information Technology Virtual University of PakistanLahorePakistan School of Information and Physical Sciences The University of NewcastleAustralia Department of Computer Science and Information Technology Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science National College of Business Administration&EconomicsLahorePakistan Sir Syed College of Computer Science University of Engineering and TechnologyLahorePakistan Department of Data Science University of the PunjabLahorePakistan Department of ICT and Natural Sciences Norwegian University of Science and TechnologyTrondheimNorway

Disease diagnosis is a challenging task due to a large number of associated *** in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated *** paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various *** Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle *** proposed system differentiates new cases provided symptoms of the ***,it becomes a time-sensitive task to discriminate symptomatic *** proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and *** coefficients have been employed to predict and compute the identified disease’s severity for each sign of *** study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and *** study used the FIS method to figure out the disease over the use of given data related to correlating with input *** tool is utilised for the implementation of *** procedure on the aforementioned given data presents that affectionate disease can derive from the *** results of our proposed method proved that FIS could be utilised for the diagnosis of other *** study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

关键词： Disease diagnosis system COVID-19 healthcare biomedical rules extraction

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KEMoS: A knowledge-enhanced multi-modal summarizing framework for Chinese online meetings

KEMoS: A knowledge-enhanced multi-modal summarizing framewor...

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作者： Qi, Peng Sun, Yan Yao, Muyan Tao, Dan School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

The demand for "online meetings" and "collaborative office work" keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing meeting summarizing models overlook the utilization of multi-modal information and the information offsetting during summarizing. In this paper, we develop a knowledge-enhanced multi-modal summarizing framework. Firstly, we construct a three-layer multi-modal meeting knowledge graph, including basic, knowledge, and multi-modal layer, to integrate meeting information thoroughly. Then, we raise a topic-based hierarchical clustering approach, which considers information entropy and difference simultaneously, to capture the semantic evolution of meetings. Next, we devise a multi-modal enhanced encoding strategy, including a sentence-level cross-modal encoder, a joint loss function, and a knowledge graph embedding module, to learn the meeting and topic-level presentations. Finally, when generating summaries, we design a topic-enhanced decoding strategy for the Transformer decoder which mitigates semantic offsetting with the aid of topic information. Extensive experiments show that our proposed work consistently outperforms state-of-the-art solutions on the Chinese meeting dataset, where the ROUGE-1, ROUGE-2, and ROUGE-L are 49.98%, 21.03%, and 32.03% respectively. © 2024 Elsevier Ltd

关键词： Signal encoding

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A model for safe lane changing of connected vehicles based on quintic polynomial Trajectory planning 7

A model for safe lane changing of connected vehicles based o...

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7th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2022

作者： Liu, Yunxiang Wang, Yao Wang, Xige Tao, Chenghao Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai China

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

For the purpose of improving the safety and comfort of lane-changing trajectory planning for future autonomous vehicles and ensure the effectiveness of lane-changing trajectory planning, a safe lane-changing model based on quintic polynomial trajectories is established in this paper. Firstly, the lane-changing scene is classified and modeled, and the lane-changing trajectory in complex environment is analyzed, and the lane-changing process is divided into two stages. Secondly, the lane-changing safety distance model is established, and the calculation method of the safety distance during the lane-changing process is given. Finally, a lane changing scene under complex road conditions is designed, and a simulation experiment is carried out. The lane-changing trajectories given by the model proposed in this paper are proved to be effective and comply with the safety and comfort constraints, which provide certain guiding significance for the lane-changing trajectory planning of autonomous vehicles in the real world. © 2022 IEEE.

关键词： Autonomous vehicles

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A Comparison of Different Optimization Techniques in AlexNet for Plant Disease Classification: SGD, Adadelta, ASGD and Adagrad 5

A Comparison of Different Optimization Techniques in AlexNet...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ponnila, P. Bazila Banu, A. Bannari Amman Institute of Technology Department of Information Technology Erode Tamilnadu Sathyamangalam India Kpr Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

Plant diseases are a biggest challenge for the farmers. Agriculture relies heavily on its production, and diagnosis of plant disease in an accurate manner will help in identification of the appropriate disease. If correct identification was not performed, there might be a significant financial loss that could have an impact on both farmers and the national economy. For resolving this issue, the AlexNet was employed, a convolutional neural network method. In this research, AlexNet is used to classify the diseases occurring in chilli, cauliflower and sunflower. This AlexNet model, with Stochastic Gradient Descent(SGD) algorithm optimization techniques had achieved 82% accuracy for the overall classification of the test images of chilli plant, 98% accuracy for the overall classification of the test images of cauliflower plant and 91% accuracy for the overall classification of the test images of sunflower plant;with Adadelta optimization 86%, 99% and 93% accuracy was achieved for the overall classification of the test pictures of chilli plant, cauliflower plant and sunflower plant respectively. After using the Asynchronous Stochastic Gradient(ASGD) optimization with AlexNet model, the accuracy obtained are 88% for the test images of chilli plant, 98% for the test images of cauliflower plant and 91% for the test images of sunflower plant;and finally, AdaGrad optimization with AlexNet achieved accuracy of 80% for the test images of chilli plant, 9 8% for the test images of cauliflower plant and 93% for the test images of sunflower plant. © 2024 IEEE.

关键词： Convolutional neural networks

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Transceiver Design for MIMO-DFRC systems 48

Transceiver Design for MIMO-DFRC Systems

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wen, Cai Davidson, Timothy N. Northwest University School of Information Science and Technology Xi'an China McMaster University Department of Electrical and Computer Engineering Hamilton Canada

ISBN: (纸本)9781728163277

This paper addresses joint design of the transmitting waveform and the receivers of a dual-function radar-communication (DFRC) system that enables both multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communications. The proposed approach incorporates the design of the communication receiving (Rx) coefficients, in addition to the radar Rx filters. We seek to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) over multiple targets, subject to per-antenna power constraints, peak-to-average-power ratio (PAPR) constraints and a communication SINR constraint for each user. A successive convex approximation algorithm is developed to find a good solution for the resultant nonconvex design problem. Numerical results show that by incorporating the communication Rx coefficients into the joint design, the radar and communication capabilities of the DFRC system can be significantly enhanced over the state-of-the-art designs. © 2023 IEEE.

关键词： Dual-function radar-communication systems MIMO radar MU-MISO communication

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Automatic driving path planning based on A-Star algorithm 7

Automatic driving path planning based on A-Star algorithm

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7th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2022

作者： Liu, Yunxiang Wang, Yilin Wen, Zhenni Yuan, Xinxin Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

Path planning is one of the important contents of autonomous driving system research, the core of which is to accurately and rapidly search the optimal path between the starting point and the end point. A-Star algorithm is A path - finding algorithm which uses heuristic information to improve search efficiency. In this paper, the path planning of automatic driving is realized based on the A-Star algorithm, and the lateral control strategy is replaced by the traditional pure tracking method with Stanley track tracking method to optimize the control scheme, further meeting the requirements of real-time, high efficiency and accuracy of the decision control system. The automatic driving system was built in PreScan and Simulink co-simulation environment to explore the efficiency of the path planning algorithm and the control ability of the trajectory tracking algorithm. The error between the path trajectory and the target ideal path under the control of the algorithm is compared and analyzed in the simulation environment to prove the feasibility and effectiveness of the algorithm. Finally, the algorithm is deployed on the autonomous driving vehicle platform to verify that it can meet the requirements of autonomous driving of small unmanned vehicles. © 2022 IEEE.

关键词： Motion planning

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Forecasting leading economic indicators in the US from financial news using multi-task learning

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Soft Computing 2025年第7期29卷 3641-3657页

作者： Ying, Josh Jia-Ching Liu, Chia-Chen Tseng, Vincent S. Zhang, Wenbin Zhang, Ji Department of Management Information Systems National Chung Hsing University Taichung402 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Computer Science Michigan Technological University HoughtonMI49931 United States School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD4350 Australia

Leading economic indicators are crucial statistics that are based on the economy and have a significant impact on various factors such as policies, stock market trends, etc. However, due to the long release time interval of these indicators, it is challenging to accurately predict their trends in advance. In this paper, we propose an effective framework for forecasting leading economic indicators in the United States by utilizing leading indicators to predict each other and learn their mutual correlation through an attention mechanism. To achieve this, we present a hierarchical, multi-task learning approach that uses textual data to make predictions for four essential leading economic indicators. We first group news articles by topic and then summarize and extract information using a time-series method and attention. Finally, we concatenate news information with historical data to forecast the leading economic indicators. Our experimental results demonstrate that our approach successfully forecasts leading economic indicators, and we are able to predict four indicators one month beforehand. This framework has significant potential for use in the finance industry and can be used to inform important decisions related to investment strategies, policymaking, and other financial aspects. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Voting Based Ensemble Framework For Crop Recommendation 7

Voting Based Ensemble Framework For Crop Recommendation

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7th International Conference on Image information Processing, ICIIP 2023

作者： Aryaman, Mayank Paliwal, Yashica Sharma, Aman Mohana, Rajni Jaypee University of Information Technology Computer Science And Engineering Department Waknaghat India Cse Jaypee University of Information Technology Waknaghat India Cse Amity School of Engineering Chandigarh India

ISBN: (纸本)9798350371406

Agriculture holds paramount importance for a significant portion of India's population, contributing significantly to the national economy. However, many Indian farmers rely on traditional senses rather than data-driven insights for crop selection, risking negative impacts on their mental and financial well-being. This article emphasizes the potential of Machine Learning models, utilizing a comprehensive dataset combining factors like rainfall, climate, humidity, and soil nutrients, to guide farmers in making informed decisions for optimal crop selection. To address the challenges faced by Indian farmers, the proposed methodology involves the application of Machine Learning models to a dataset encompassing crucial parameters such as sowing season, soil properties, climate, and location. By leveraging advanced algorithms, the models aim to recommend crops tailored to the farm's specific conditions, ensuring precision and accuracy in decision-making. This data-driven approach seeks to revolutionize traditional farming practices and enhance overall agricultural outcomes. The application of Machine Learning models on a comprehensive dataset demonstrated a robust recommendation system for crop selection, considering factors like soil health and weather conditions. This technology-driven approach empowers Indian farmers with informed decision-making, mitigating risks and fostering sustainable agricultural practices. © 2023 IEEE.

关键词： Crops

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