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Hunger games search optimization with deep learning model for sustainable supply chain management

Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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DEVELOPABLE RULED SURFACES GENERATED BY THE CURVATURE AXIS OF A CURVE

arXiv

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arXiv 2025年

作者： Taş, Ferhat Ziatdinov, Rushan Department of Computer Science Faculty of Science Istanbul University Istanbul34143 Turkey Department of Industrial Engineering College of Engineering Keimyung University Daegu704-701 Korea Republic of

MSC Codes 53Axx, 65D17Ruled surfaces play an important role in various types of design, architecture, manufacturing, art, and sculpture. They can be created in a variety of ways, which is a topic that has been the subject of much discussion in mathematics and engineering journals. In geometric modelling, ideas are successful if they are not too complex for engineers and practitioners to understand and not too difficult to implement, because these specialists put mathematical theories into practice by implementing them in CAD/CAM systems. Some of these popular systems such as AutoCAD, Solidworks, CATIA, Rhinoceros 3D, and others are based on simple polynomial or rational splines and many other beautiful mathematical theories that have not yet been implemented due to their complexity. Based on this philosophy, in the present work, we investigate a simple way to generate ruled surfaces whose generators are the curvature axes of curves. We show that this type of ruled surface is a developable surface and that there is at least one curve whose curvature axis is a line on the given developable surface. In addition, we discuss the classifications of developable surfaces corresponding to space curves with singularities, while these curves and surfaces are most often avoided in practical design. Our research also contributes to the understanding of the singularities of developable surfaces and, in their visualisation, proposes the use of environmental maps with a circular pattern that creates flower-like structures around the singularities. Copyright © 2025, The Authors. All rights reserved.

关键词： Visualization

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Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

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computers, Materials & Continua 2023年第8期76卷 1537-1554页

作者： Badriyya BAl-onazi Najm Alotaibi Jaber SAlzahrani Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Mahmoud Othman Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi 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

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or *** the number of labels is limited to one,the task becomes single-label text *** Arabic texts include unstructured information also like English texts,and that is understandable for machine learning(ML)techniques,the text is changed and demonstrated by numerical *** recent times,the dominant method for natural language processing(NLP)tasks is recurrent neural network(RNN),in general,long short termmemory(LSTM)and convolutional neural network(CNN).Deep learning(DL)models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection,medical image analysis,and so *** paper introduces aModified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition(MDFO-EMTRR)model *** presentedMDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic *** achieve this,theMDFO-EMTRRtechnique encompasses data pre-processing to transform the input data into compatible ***,the ELM model is utilized for the representation and recognition of the Arabic *** last,the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier *** experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

关键词： Arabic corpus dragonfly algorithm machine learning text mining extreme learning machine

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Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation

Exploration and Analysis of Seizure Spikes Through Spectral ...

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International Conference on Business Intelligence and Information Technology, BIIT 2023

作者： Najmusseher Nizar Banu, P.K. Azar, Ahmad Taher Kamal, Nashwa Ahmad Alzahrani, Abdulkareem Central Campus Karnataka Bangalore India College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha13518 Egypt Faculty of Engineering Cairo University Giza Egypt Department of Computer Engineering and Science Faculty of Computer Science and Information Technology Al-Baha University Al-Baha Saudi Arabia Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9789819739790

Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The model’s performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. © The Author(s), under exclusi

关键词： Fast Fourier transforms

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Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

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computers, Materials & Continua 2022年第4期71卷 143-157页

作者： Anwer Mustafa Hilal Badria Sulaiman Alfurhood Fahd N.Al-Wesabi Manar Ahmed Hamza Mesfer Al Duhayyim Huda G.Iskandar Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

关键词： Smart city sentiment analysis artificial intelligence healthcare management metaheuristics deep learning parameter tuning

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A Review of an off Grid Solar DC System for Rural Houses 7

A Review of an off Grid Solar DC System for Rural Houses

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7th International Conference on Intelligent Computing and control systems, ICICCS 2023

作者： Pratheeba, C. Muthuvinayagam, M. Siva Ramkumar, M. Rohith Bhat, C. Maniraj, P. Kumar, Neelam Sanjeev Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Mahendra Engineering College Department of Electrical and Electronics Engineering Namakkal Mallasamudram India Karpagam Academy of Higher Education Faculty of Engineering Department of Electrical and Electronics Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Thalavapalayam Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India SIMATS Saveetha School of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350397253

Despite its importance. more than 800 million people still lack access to electricity. Individuals in many rural areas lack access to a consistent supply of energy due to the high cost of grid extension. Off-grid solar systems enable rural residents to connect to the energy 2rid and power their homes' lights. appliances. and other electronic equipment. This document provides a brief overview of a solar power system that can be used to Dower remote homes. Solar panels. a charge controller. batteries. and an inverter make up the system. Sunlight is converted into direct current by solar panels. which is then stored in batteries. The charge controller regulates the charging and discharging cycles of the battery pack in order to keep the cells healthy and long-lasting. The inverter converts the stored DC electricity into the alternating current (AC) power that appliances and other AC loads require. The proposed method is intended to be simple and low-cost. making it an appropriate option for regions with limited resources and low growth. The system can be customized to meet the exact energy needs of the house by adding additional solar panels or batteries. Furthermore. the structure is made up of discrete components. making it easier to assemble and maintain. Off-grid solar power systems are a cost-effective and efficient wav of bringing electricity to areas that are not connected to the power grid. The proposed system describes the design and installation of a solar-powered. direct current (DC) power source for isolated dwellings. The strategy promotes long-term economic growth and raises the living standards of rural families. © 2023 IEEE.

关键词： Solar panels

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Analysis of YouTube Video Comments with NLP Methods

Analysis of YouTube Video Comments with NLP Methods

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Pirnau Mironela Priescu Iustin Priescu Catalina Mihaela Pirnau Claudiu Ghiculescu Liviu Daniel Department of Informatics Faculty of Informatics “Titu Maiorescu” University Bucharest Romania Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania Manufacturing Engineering Department Faculty of Industrial Engineering and Robotics National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of “ChatGPT, AI clone, AI robot and Deep Learning.”

关键词： Video on demand Social networking (online) Education Tutorials Chatbots Web sites Artificial intelligence

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Non-Destructive pH Sensor using Honeycomb Resonator for Liquid-Mixture Characterization 4

Non-Destructive pH Sensor using Honeycomb Resonator for Liqu...

引用

4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Chudpooti, Sukanya Sangpet, Patchadaporn Robertson, Ian D. Akkaraekthalin, Prayoot Somjit, Nutapong Chudpooti, Nonchanutt Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (纸本)9798350330618

This paper presents a feasibility study of a pH sensor utilizing the microwave transmission technique within the CST Microwave Studio software. The resonator is designed based on the modified split ring resonator shape (SRR) by incorporating six segments in a circular shape. The proposed resonator shape resembles a honeycomb structure with a capacitive gap to enhance the electric field (E-field) distribution in its area. The operating frequency is set to the Industrial, Scientific, and Medical (ISM) band of 5.82 GHz. According to the simulation, the E-field demonstrates its maximum strength at the focused area, measuring 29,863 V/m. Consequently, this position is deemed suitable for selecting the sensing area for liquid-mixture detection. To investigate the performance of the proposed sensor, the industry model of a Teflon tube is imported and placed atop the focused honeycomb resonator. The performance of the proposed sensor is evaluated by modeling various liquid samples, adjusting electrical properties such as relative permittivity, Εr, and loss factor. The simulated results indicate that the proposed sensor provides a satisfactory transmission coefficient, S21, response for classifying relative permittivity, Εr, changes within the range of 1 to 81. However, for future development, the proposed sensor will undergo fabrication and further investigation to assess its performance in practical applications. ©2023 IEEE.

关键词： Microwave sensors

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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An Improved Convolutional Neural Network for Medical Image Segmentation

An Improved Convolutional Neural Network for Medical Image S...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Obaidi Al-Khafaji, Safaa Hakeem Palle, Kowstubha Thingbaijam, Lenin Prasad, P. Venkata Mallala, Balasubbareddy Pund, Sachin College of Dentistry University of Babylon University of Babylon Computer Science Babylon Iraq Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India Mlcu Faculty It India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350329773

This paper provides a progressed convolutional neural community (CNN) version for medical picture segmentation. It specializes in improving the accuracy and speed of today's segmenting of scientific photos and improving modern semantic segmentation of first-rate present-day clinical photos. Modern accuracy is improved by introducing pre-processing layer, residual connection, attention module, and up sampling layer to the CNN. The stepped-forward overall performance state-of-the-art pace is achieved through dual-project cutting-edge, wherein the segmentation task is divided into two sequential sub-obligations, modern-day semantic segmentation and shape regression. The improved version is examined on publicly available scientific datasets, achieving 49a2d564f1275e1c4e633abc331547db segmentation performance. The results show that the proposed method is robust, green, and generalizable.. © 2023 IEEE.

关键词： Semantic Segmentation

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