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Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2020年 28卷

作者： Karadag, Kerim Tenekeci, Mehmet Emin Tasaltin, Ramazan Bilgili, Aysin Harran Univ Osmanbey Campus Elect Elect Engn Dept Sanliurfa Turkey Harran Univ Osmanbey Campus Comp Engn Dept Sanliurfa Turkey GAPTAEM Sanliurfa Turkey

The development of computerized automated diagnostic systems ensures more effective health screening in plants. In this way, the damage caused by diseases can be reduced by early detection. Light reflections from plant leaves are known to carry information about plant health. In the study, healthy and fusarium diseased peppers (capsicum annuum) was detected from the reflections obtained from the pepper leaves with the aid of spectroradiometer. Reflections were taken from four groups of pepper leaves (healthy, fusarium-diseased, mycorrhizal fungus, fusarium-diseased and mycorrhizal fungus) grown in a closed environment at wavelengths between 350 nm and 2500 nm. Pepper disease detection takes place in two stages. In the first step, the feature vector is obtained. In the second step, the feature vectors of the input data are classified. The feature vector consist of the coefficients of wavelet decomposition and the statistical values of these coefficients. Artificial Neural Networks (ANN), Naive Bayes (NB) and K-nearest Neighbor (KNN) were used for classification. In detection the health case of pepper, the average success rates of different classification algorithms for the first two groups (diseased and healthy peppers) were calculated as 100% for KNN, 97.5% for ANN and 90% for NB. Likewise, these rates for the classification of all groups were calculated as 100% for KNN, 88.125% for ANN and 82% for NB. Overall, the results have shown that leaf reflections can be successfully used in disease detection. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Pepper disease detection Spectral reflectance machine learning algorithms Wavelet Classification

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Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 356卷 120560页

作者： Zhang, Maomao Tan, Shukui Liang, Jinshui Zhang, Cheng Chen, Enqing Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430079 Peoples R China Dalian Ocean Univ Coll Marine Technol & Environm Dalian 116023 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430079 Peoples R China Wuhan Donghu Univ Sch Educ & Foreign Languages Wuhan 430212 Peoples R China

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km(2) in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20( degrees)C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 C-degrees is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

关键词： Urban land expansion Thermal environment Sustainable development machine learning algorithms China

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Application of Healthcare Management Technologies for COVID-19 Pandemic Using Internet of Things and machine learning algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2023年 1-22页

作者： Ismaeel, Nooruldeen Q. Mohammed, Husam Jasim Chaloob, Ibrahim Zeghaiton Kwekha-Rashid, Ameer Sardar Alhayani, Bilal Alkhayyat, Ahmed Abbas, Sara Taher Dauwed, Mohammed Alkawak, Omar A. Ibn Sina Univ Med & Pharmaceut Sci Baghdad Iraq Al Karkh Univ Sci Baghdad Iraq Al Esraa Univ Coll Baghdad Iraq Univ Sulaimani Business Informat Technol Sulaymaniyah Iraq Yildiz Tech Univ Istanbul Turkiye Islamic Univ Tech Engn Coll Najaf Iraq Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Engn Tech Baghdad Iraq Dijlah Univ Coll Dept Med Instrumentat Tech Engn Baghdad 10022 Iraq Univ Babylon Coll Engn Al Mussaib Dept Energy Engn Babil Iraq

Internet of Things (IoT) has acquired persuading research ground as another examination subject under big assortment regards scholarly and modern disciplines, particularly under healthcare. IoT transformation has been reconstructing current healthcare frameworks through consolidating innovative, financial, and social possibilities. It was developing health care frameworks through customary to extra customized healthcare frameworks by kinds of patients may analyzed, handled, and checked all the extra without any problem. Since from the time of pandemic began, there was quick exertion under various examination networks to take advantage of a big assortment of advances to battle this overall danger, and IoT innovation is one of pioneers around here. IoTs sensor-based innovation gives a brilliant capacity to decrease the danger of medical procedure during convoluted cases and supportive for COVID-19 sort pandemic. In the clinical field, IoTs centre is to assist with playing out the treatment of various COVID-19 cases unequivocally. It makes the specialist work simpler through limiting dangers and expanding general presentation. Through utilizing this innovation, specialists can undoubtedly distinguish changes in basic boundaries of the COVID-19 patient. This paper overviews the job of IoT based advancements under COVID-19 and surveys the best in class structures, stages, applications, and modern IoT based arrangements fighting COVID-19 of every three primary stages, including early conclusion, quarantine time, and after recuperation. In conclusion, the paper is revealing that all machine-learning algorithms tested in this study can be used in the prediction of healthcare with a high accuracy;however, the SVM and K-NN algorithms are the best fitting algorithms among all algorithms. Then Naive Bayes, Decision Table, and Decision Stump follow it respectively.

关键词： IoT COVID-19 Healthcare system Medical Corona virus machine learning algorithms

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A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting

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BIOMETRICAL JOURNAL 2019年第4期61卷 1049-1072页

作者： Cannas, Massimo Arpino, Bruno Univ Cagliari Dept Econ & Business Sci Viale St Ignazio 84 I-09123 Cagliari Italy Univ Firenze Dept Stat Comp Sci Applicat Florence Italy

Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance. Using simulations, we compare the performance of PSM and PSW based on logistic regression and machine learning algorithms (CART;Bagging;Boosting;Random Forest;Neural Networks;naive Bayes). Additionally, we consider several measures of covariate balance (Absolute Standardized Average Mean (ASAM) with and without interactions;measures based on the quantile-quantile plots;ratio between variances of propensity scores;area under the curve (AUC)) and assess their ability in predicting the bias of PSM and PSW estimators. We also investigate the importance of tuning of machine learning parameters in the context of propensity score methods. Two simulation designs are employed. In the first, the generating processes are inspired to birth register data used to assess the effect of labor induction on the occurrence of caesarean section. The second exploits more general generating mechanisms. Overall, among the different techniques, random forests performed the best, especially in PSW. Logistic regression and neural networks also showed an excellent performance similar to that of random forests. As for covariate balance, the simplest and commonly used metric, the ASAM, showed a strong correlation with the bias of causal effects estimators. Our findings suggest that researchers should aim at obtaining an ASAM lower than 10% for as many variables as possible. In the empirical study we found that labor induction had a small and not statistically significant impact on caesarean section.

关键词： caesarean section covariate balance labor induction machine learning algorithms propensity score methods

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Seafood Quality Detection Using Electronic Nose and machine learning algorithms With Hyperparameter Optimization

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IEEE ACCESS 2023年 11卷 62484-62495页

作者： Wijaya, Dedy Rahman Syarwan, Nailatul Fadhilah Nugraha, Muhammad Agus Ananda, Dahliar Fahrudin, Tora Handayani, Rini Telkom Univ Sch Appl Sci Bandung 40257 Indonesia Surabaya Telkom Inst Technol Dept Software Engn Surabaya 60231 Indonesia

The fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted in a growing market demand for high-quality and safe products. Meeting this demand necessitates the maintenance of freshness in marine fishery products. Thus, this research aims to develop a fast, cheap, accurate method utilizing an electronic nose (e-nose) and machine learning algorithms as an alternative method for assessing the freshness quality of marine fishery products (seafood). This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. machine learning algorithms are used for classification and regression tasks. The objective is to detect the freshness quality of marine fishery products accurately (classification task) while also identifying microbial populations present in the seafood (regression task). Through extensive investigations, the classification and regression models, specifically employing the k-Nearest Neighbors algorithm, demonstrated remarkable performance, achieving a very high accuracy score. Furthermore, the regression model yielded an RMSE value of 0.03 and an R-2 value of 0.995, indicating the effectiveness of the approach in assessing and quantifying the quality attributes of marine fishery products.

关键词： Seafood electronic nose machine learning algorithms hyperparameter optimization classification regression

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Automated Multi-Class Seizure-Type Classification System Using EEG Signals and machine learning algorithms

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IEEE ACCESS 2024年 12卷 136524-136541页

作者： Abirami, S. Kathiravan, M. Yuvaraj, Rajamanickam Menon, Ramshekhar N. Thomas, John Karthick, P. A. Prince, A. Amalin Ronickom, Jac Fredo Agastinose Indian Inst Technol BHU Varanasi Sch Biomed Engn Computat Neurosci & Biol Lab Varanasi 221005 India Minist Def Def Bioengn & Electro Med Lab DRDO Bengaluru 560093 India Nanyang Technol Univ NTU Natl Inst Educ NIE Singapore 639798 Singapore Sree Chitra Tirunal Inst Med Sci & Technol Cognit Behav Neurol Ctr Dept Neurol R Madhavan Nayar Ctr Comprehens Epilepsy Care Thiruvananthapuram 695011 Kerala India McGill Univ Montreal Neurol Inst Montreal PQ H3A 0G4 Canada Natl Inst Technol Tiruchirappalli Dept Sensor & Biomed Technol Tiruchirappalli 620015 Tamil Nadu India BITS Pilani Dept Elect & Elect Engn KK Birla Goa Campus Zuarinagar 403726 Goa India

Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. The treatment for epilepsy is influenced by the types of seizures. Therefore, developing a reliable, explainable, and automated system to identify seizure types is necessary. This study aims to automate the process of classification of five seizure types: focal non-specific, generalized, complex partial, absence, and tonic-clonic using electroencephalogram (EEG) signals and machine learning algorithms. The EEG signals of 2933 seizures from 327 patients were obtained from the publicly available Temple University Hospital dataset. Initially, the signals were preprocessed using a standard pipeline, and 110 features from the time, frequency, and time-frequency domain were computed from each seizure. Further, the features were ranked using the statistical test and extreme Gradient Boosting (XGBoost) algorithm to identify the significant features. We built binary and multiclass seizure-type classification systems using the identified features and machine learning algorithms. Our study revealed that the EEG band power between 11-13 Hz, 27-29 Hz, intrinsic mode function (IMF) band power 19-21 Hz, and delta band (1-4 Hz) played a crucial role in discriminating the seizures. We achieved an average accuracy of 88.21% and 69.43% for the binary and multiclass seizure-type classification, respectively, using the XGBoost classifier. We also found that the combination of features performed well compared to any single domain. This automated system has the potential to aid neurologists in making diagnosis of epileptic seizure types. The proposed methodology can be applied alongside the established clinical approach of visual evaluation for the classification of seizure-types.

关键词： Electroencephalography Feature extraction Time-frequency analysis Epilepsy machine learning algorithms Classification algorithms seizure type classification electroencephalography feature extraction machine learning

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Experimental Investigation of Body-Centric Indoor Localization Using Compact Wearable Antennas and machine learning algorithms

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第2期70卷 1344-1354页

作者： Bharadwaj, Richa Alomainy, Akram Koul, Shiban K. IIT Delhi Ctr Appl Res Elect Microwave & RF Grp New Delhi 110016 India Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5-3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx-Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology.

关键词： Location awareness Classification algorithms machine learning algorithms Wireless communication Delays Ultra wideband antennas Indoor environment Body-centric communication channel characterization indoor localization machine learning (ML) wearable antennas

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Review on Traffic Prediction Framework Using machine and Deep learning algorithms

Review on Traffic Prediction Framework Using Machine and Dee...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Anto Pravin Nancy Noella R S Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Management of road traffic and congestion is a critical feature of urban planning and infrastructure development. Traffic congestion has recently become a major problem on a global scale due to the rapid increase in industrialization and the number of vehicles on the road. Efficient traffic management not only enhances the flow of vehicles but also contributes to reducing fuel consumption, minimizing air pollution, and improving road safety. Traditional traffic management methods, which primarily rely on static models and historical data, have proven to be insufficient in handling the complexities of modern transportation systems. Therefore, there is a growing need for advanced techniques that can predict traffic patterns and optimize traffic flow in real time. Techniques of machine learning and deep learning have become increasingly effective in recent years for addressing the difficulties in controlling traffic and predictions. By influencing the vast amount of data generated from traffic sensors, GPS devices, and other sources, machine learning and deep learning algorithms can analyze, learn, and predict traffic conditions more accurately than traditional methods. These techniques are particularly effective in handling the dynamic and nonlinear nature of traffic flow, making them ideal for real-time applications in traffic control, congestion prediction, and incident management.

关键词： Deep learning machine learning algorithms Heuristic algorithms Transportation Traffic control Prediction algorithms Real-time systems Vehicle dynamics Traffic congestion Optimization

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Using Physiological Signals and machine learning algorithms to Measure Attentiveness During Robot-Assisted Social Skills Intervention: A Case Study of Two Children with Autism Spectrum Disorder

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2023年第3期26卷 39-45页

作者： Welch, Karla Conn Pennington, Robert Vanaparthy, Saipruthvi Do, Ha Manh Narayanan, Rohit Popa, Dan Barnes, Gregory Kuravackel, Grace Univ Louisville Elect & Comp Engn Dept Louisville KY 40292 USA Univ N Carolina Special Educ Charlotte NC USA ASML Developing High Speed Controls Syst Photolithog Ma San Diego CA USA Plume Design Palo Alto CA USA Princeton Univ Dept Elect & Comp Engn Princeton NJ USA Univ Louisville Louisville Automat & Robot Res Inst Louisville KY USA Univ Louisville Elect & Comp Engn Dept Louisville KY USA Univ Louisville Norton Childrens Autism Ctr Louisville KY USA Univ Louisville Dept Neurol Louisville KY USA Univ Louisville Dept Pediat Louisville KY USA

Individuals with autism spectrum disorder (ASD) often face barriers in accessing opportunities across a range of educational, employment, and social contexts. One of these barriers is the development of effective communication skills sufficient for navigating the social demands of everyday environments. Fortunately, researchers have established evidence-based practices (EBP) for teaching critical communication skills to individuals with ASD [1]. One EBP that has received a great deal of attention over the last few decades is technology-aided instruction and intervention (TAII) [1], [2]. TAII is an instructional practice in which technology is an essential component and is used to facilitate behavior change. Further, it encompasses a wide range of applications including computer-assisted instruction, virtual and augmented reality, augmentative and alternative communication, and robot-assisted intervention [2].

关键词： Autism Pediatrics machine learning algorithms Navigation Employment Education Physiology

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Early Detection of Chronic Diseases Using machine and Deep learning algorithms

Early Detection of Chronic Diseases Using Machine and Deep L...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Bhushan Fulkar Trishul Dhale Utkarsha Pacharaney Shruti Deshmukh Department of Artificial intelligence And Data science Datta Meghe institute of Higher Education and Research Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Today's persons suffer from a wide variety of diseases due to various influences and choices made at the community level. Thus, to prevent the occurrence of such illnesses persistent identification and prediction are paramount. Manually determining the disorders is generally challenging for the doctors to be accurate with the exact numbers. The objective of this study is thus to use these chronic conditions and an accompanying test to select people who will be predominantly affected by these diseases. This might be done by making certain that this categorization correctly singles out people with chronic diseases employing the most advanced data learning technique. Another tough issue is a prediction concerning health conditions. It can therefore be concluded that data mining is an important component of sickness prediction. Employing automatic feature extraction and disease prediction by CNN, distance computation employing KNN for identification of the closest match or data within the data set, and the last disease prognosis of a broad disease, the proposed system enables the determination of patient's symptoms. The patient's life history, medical history of consultations, and illness symptoms were used to construct the data client set. It is on this comprehensive sickness prediction that all of these details were considered. The above said method is compared with logistic regression, Decision tree and Naive Bayes in the conclusion part of the paper.

关键词： Deep learning Accuracy machine learning algorithms Medical services Feature extraction Classification algorithms History Convolutional neural networks Diseases Biomedical imaging

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