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Supply Chain Planning of Agricultural-Products Under Water Scarcity Using Deep Learning

Iranian Journal of science and Technology - Transactions of Civil engineering 2025年 1-14页

作者： Sampath, P. Mahariba, A. Jackulin Sathiyamurthi, P. Sridharan, S. Department of Computer Science and Engineering - Cyber Security Easa College of Engineering and Technology Tamil Nadu Coimbatore India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electrical and Electronics Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

All people on the planet expected agriculture to provide them with food to survive. The process of producing agricultural goods while integrating logistical elements is labor-intensive. Additionally, the lack of water exacerbated several issues with agricultural product production. The researchers did not adequately address the issues surrounding the lack of water over food chains. For supply chain management, this paper presented a novel deep learning based optimization mechanism to address the agricultural production challenges under water scarcity. Hence, this study proposed a Dynamic Salp Swarm Optimization (DSS) algorithm for feature selection. The DenseNet121 based Modified Runge–Kutta Optimization (MRKO) is used to predict the water usage. For supply chain optimization, unique technique called Beetle Swarm Optimization (BSO) proposed. In addition, this reduces production costs and time while increasing profitability. The Python simulator is used to perform simulations and implement the processes. According to the similar study, the proposed strategy outperforms all other methods in terms of supply chain manufacturing cost, time, accuracy, and recall for water consumption predictions. The overall sustainability enhanced and the agricultural supply chain water scarcity impact mitigated with these findings and it describes the potential of proposed work compared to state-of-art. From the comparative study, the proposed work demonstrated 96.81% accuracy, 95.7% precision and 96.8% recall with3.11% MSE, 2.10% MAE, and 3.10% RMSE results. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Agri-products Supply chain Water consumption and DenseNet121 Water scarcity

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Diabetic Retinopathy Using Deep Learning

Diabetic Retinopathy Using Deep Learning

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2023 International Conference on computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Joshi, Shivani Kumar, Rajiv Rai, Praveen Kumar Garg, Sanskar G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering India

ISBN: (纸本)9798350323917

According to a recent survey by WHO, every fourth person is diabetic. It can be due to lifestyle, pollution, heredity or many other reasons. A diabetic person with the time has some threats in life. One of these threats is Retinopathy which is a well-known microvascular consequence of diabetes mellitus that poses a risk to vision is diabetic retinopathy. Worldwide, 93 million people have diabetic retinopathy at this time. Studies on the prevalence of diabetic retinopathy and risk factors are lacking. People who have diabetes may have more chances to develop diabetic retinopathy, an eye disorder that can lead to blindness. The retina's blood vessels are impacted (the light-sensitive layer of tissue in the back of your eye). It's crucial to undergo a thorough dilated eye exam at least once a year if you have diabetes. In this paper, we have used the deep learning method by taking a single snapshot of the human fundus for detecting the stage of diabetic retinopathy. We have also suggested the multistage transfer learning method, which uses comparable datasets with various labelling. With a sensitivity and specificity of 0.99, the proposed method can be used as early diagnosis of diabetic retinopathy on APTOS 2019 dataset for Detecting Blindness (13000 images). We employed a deep learning model Shapley Additive exPlanations (SHAP) for game theoretic strategies to research the diagnosis of diabetic retinopathy. In order to associate optimal credit allocation with local explanations, it makes use of the conventional Shapley values from game theory and their related extensions. To understand the predictions of our model and how to further improve its performance, in this study we will train a base ResNet50 model, evaluate it, and use the SHAP model's explain ability technique. © 2023 IEEE.

关键词： Blood vessels

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Health Care Support for Elderly People in Resource Poor Areas 1st

Health Care Support for Elderly People in Resource Poor Area...

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1st International Conference on Deep sciences for Computing and Communications, IconDeepCom 2022

作者： Kaladevi, R. Revathi, A. Gayathri, A. Department of Computer Science and Engineering Saveetha Engineering College Tamilnadu Chennai602105 India Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu Kattankulathur603 203 India Chennai India

ISBN: (纸本)9783031276217

Healthcare is an essential service as a well as high revenue yielding sector. There has been increasing demand for chronic care in rural area. On the other hand, the Healthcare industry is combating against workforce crisis due to inadequate infrastructure, shortage of doctors, specialists, and medical assistants. The phenomenal growth of computing and connectivity has redefined the Healthcare environment with the help of Information and Communication Technologies (ICT). Life span of people has increased with technological support and statistics reveals that by 2030 the population of elderly people (above 65 years) will become double. This automatically increases chronic diseases and aging related ailments in the society. After doing careful analysis this chapter aims to suggest better algorithm for which considers people above 50 years of age who need continuous medical support due to the increase of chronic diseases and insufficient medical assistance. The Linear classifier SVM and parallel ensemble Random Forest Algorithm are applied appropriately to predict the disease. The patient who suffer from more than one ailment may take multiple and repetitive pills which results in adverse side effects. The proposed system coordinates these issues through decision support system and reduces unnecessary medications and side effects. The proposed system classifies diseases and their severity and suggests medications without any conflict. This will reduce human effort, time, cost, unnecessary medication and side effects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Detection of Medicinal Plants Using Machine Learning 12th

Detection of Medicinal Plants Using Machine Learning

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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Using wearable sensors to study the brain-heart interactions during attentional and resting states

Using wearable sensors to study the brain-heart interactions...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lee, Jia-Jeng Chen, Chun-Chuan Wu, Eric Hsiao-Kuang Yeh, Shih-Ching Wang, Wei-Jen National Central University Laboratory of Computational Neuroscience Department of Biomedical Sciences and Engineering Taoyuan Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

Wearable sensors have a significant increase in both research and commercialization as a kind of consumer electronics. In the field of healthy science, wearable sensors provide affordable solutions for massive screening or long tern monitoring, for instance, wearable dry electroencephalography (EEG) for brain and electrocardiogram (ECG) for heart. Human brain and heart are the most important organs and the targets for healthy monitoring. Given that brain and heart sometimes have comorbidities of each other as they are reciprocally connected, it is important to monitor their relations. However, only a few studies addressed the relationships between them under healthy states. This study aims to examine the brain-heart interactions(BHI) under different states, including two different resting states and one attentional state using wearable sensors. Twenty subject were recruited and performed a memory task in a virtual supermarket. Five-channel dry EEG and two-channel ECG were acquired before, during, and after the task. Pearson correlation was employed to analyze the relations between EEG features and heart rate variability (HRV). Two sample t test and machine learning method were employed to analysis the difference between states. We found that higher frequency oscillations in the brain were used to communicate with the heart during the pre-rest state and task state respectively, then switched to lower frequency for resetting the BHI after task. Moreover, the combination of EEG and ECG features can best distinguish the pre and post resting states. Our findings suggest that BHI are dynamic and state-dependent and studying the BHI can provide better understanding of the states in the body to aid the diagnosis and/or treatment for diseases affected both brain and heart. © 2023 IEEE.

关键词： Electroencephalography

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Handling Big Data in Relational Database Management Systems

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computers, Materials & Continua 2022年第9期72卷 5149-5164页

作者： Kamal ElDahshan Eman Selim Ahmed Ismail Ebada Mohamed Abouhawwash Yunyoung Nam Gamal Behery Faculty of Science Al-Azhar UniversityCairoEgypt Faculty of Computer and Artificial Intelligence Damietta UniversityEgypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of *** paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.

关键词： Big data RDBMS NoSQL DBMSs MongoDB MySQL unstructured data semi-structured data

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Non-Negative Minimum Volume Factorization (NMVF) for Hyperspectral Images (HSI) Unmixing: A Hybrid Approach

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computers, Materials & Continua 2022年第11期73卷 3705-3720页

作者： Kriti Mahajan Urvashi Garg Nitin Mittal Yunyoung Nam Byeong-Gwon Kang Mohamed Abouhawwash Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Spectral unmixing is essential for exploitation of remotely senseddata of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members andtheir matching fractional, draft rules abundances for every pixel in HSI. Thispaper aims to unmix hyperspectral data using the minimal volume methodof elementary scrutiny. Moreover, the problem of optimization is solved bythe implementation of the sequence of small problems that are constrainedquadratically. The hard constraint in the final step for the abundance fractionis then replaced with a loss function of hinge type that accounts for outlinersand noise. Existing algorithms focus on estimating the endmembers (Ems)enumeration in a sight, discerning of spectral signs of EMs, besides assessmentof fractional profusion for every EM in every pixel of a sight. Nevertheless, allthe stages are performed by only a few algorithms in the process of hyperspectral unmixing. Therefore, the Non-negative Minimum Volume Factorization(NMVF) algorithm is further extended by fusing it with the nonnegativematrix of robust collaborative factorization that aims to perform all the threeunmixing chain steps for hyperspectral images. The major contributions ofthis article are in this manner: (A) it performs Simplex analysis of minimum volume for hyperspectral images with unsupervised linear unmixing isemployed. (B) The simplex analysis method is configured with an exaggeratedform of the elementary which is delivered by vertical component analysis(VCA). (C) The inflating factor is chosen carefully inactivating the constraintsin a large majority for relating to the source fractions abundance that speedsup the algorithm. (D) The final step is making simplex analysis method robustto outliners as well as noise that replaces the profusion element positivity hardrestraint by a hinge kind soft restraint, preserving the local minima havinggood quality. (E) The matrix factorizati

关键词： Hyperspectral Imaging minimum volume simplex source separation end member extraction non-negative minimum volume factorization(NMVF) endmembers(EMs)

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Comparative Analysis of Deep Learning Models for Telugu News Text Classification 21

Comparative Analysis of Deep Learning Models for Telugu News...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Settineni, Pooja Veerendra, B. Reddy, Mure Sai Jaideep Samuel, Prithi Sumathi, D. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India SRM Institute of Science Kattankulanthur Campus Department of Computational Intelligence India

ISBN: (纸本)9798350358230

Text classification is the process of labeling or classifying the text to certain categories based on the content into various text classes. There are many use cases like Natural language processing, sentimental analysis, topic detection, Spam filtering news categorization have been solved using this mechanism. We documented a comparative study of four Deep Learning (DL) algorithms to classify and categories the text for two different data sets. These datasets contain the content of Telugu news articles. The four DL models that are used in this works are Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional Gated Recurrent Unit (Bi-GRU) and bidirectional Long Short-Term Memory (Bi-LSTM) which are the variants of the recurrent neural networks (RNN) which can process sequential data such as text. The model performance will be compared using the accuracy of it as a metrics. We also have documented pros and cons of each model using the results that they admit. This paper analyzes the results and discuss the advantages and disadvantages of each model. In this paper we first start with the preprocessing techniques used for process the data and further we move to the architecture's explanation for each model. The experimental results are presented using graphs and pie charts. The findings and implications of the study are discussed. Some limitations and future directions of the work are mentioned. © 2023 IEEE.

关键词： Long short-term memory

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