The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models implemented to identify diagnose in plants from their leaves, since CNNs have achieved impressive results the field machine vision. Standard CNN require a large number parameters higher computation cost. we replaced standard convolution with depth=separable convolution, which reduces parameter were trained an open dataset...

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis required as soon possible. Machine learning technique has become reliable for medical treatment. With help a machine classifier algorithms, doctor can detect disease on time. For this perspective, prediction been discussed in article. dataset taken from UCI repository. Seven algorithms have applied research such artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic...

Landslide is a devastating natural disaster, causing loss of life and property. It likely to occur more frequently due increasing urbanization, deforestation, climate change. susceptibility mapping vital safeguard This article surveys machine learning (ML) models used for landslide understand the current trend by analyzing published articles based on ML models, causative factors (LCFs), study location, datasets, evaluation methods, model performance. Existing literature considered in this...

Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis very significant can avoid some categories cancers, such as melanoma focal cell carcinoma. The recognition the classification malignant growth in beginning time expensive challenging. deep learning architectures recurrent networks convolutional neural (ConvNets) are developed past, which proven appropriate for non-handcrafted extraction complex features. To additional expand efficiency...

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use series of physical and mental tests to diagnose AD that may not always be effective. Damage brain cells the most significant change AD. Proper analysis images assist identification crucial bio-markers for disease. Because development so intricate, traditional image processing algorithms sometimes fail perceive important bio-markers. The deep neural network...

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast modern electrical and electronic devices’ contribution increasing current total distortion factor (THDI) exceeding regulation limit, analyses based tests models of individual devices are conducted. In this article, composite approach was applied. performance currents produced by sets commonly used commercial residential facilities’ nonlinear loads...

In this research, we proposed a Deep Convolutional Neural Network (DCNN) model for image-based plant leaf disease identification using data augmentation and hyperparameter optimization techniques. The DCNN was trained on an augmented dataset of over 240,000 images different healthy diseased leaves backgrounds. Five image techniques were used: Generative Adversarial Network, Style Transfer, Principal Component Analysis, Color Augmentation, Position Augmentation. random search technique used...

This study used panel simultaneous equations models with a generalized method of moments (GMM) estimator to examine the three-way linkages between ecological footprint (EFP), renewable energy consumption (REC), and income in Group Seven (G7) countries over period 1990–2018. The outcomes this demonstrate two-way association gross domestic product (GDP) energy. findings confirm presence bidirectional link outcome footprint, as well EFP results that improving human capital positively...

Nowadays, breast cancer is the most frequent among women. Early detection a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, methods to improve accuracy of ML classification models for prognosis are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used identify important features. On these selected features...

Various plant diseases are major threats to agriculture. For timely control of different in effective manner, automated identification highly beneficial. So far, techniques have been used identify the plants. Deep learning is among most widely recent times due its impressive results. In this work, we proposed two methods namely shallow VGG with RF and Xgboost diseases. The model compared other hand-crafted deep learning-based approaches. experiments carried on three plants corn, potato,...

Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis brain tumors. Previous studies have focused normal (nontumorous) or abnormal (tumorous) MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify MRI images into abnormal. Gender age added higher attributes for more accurate meaningful classification. A Convolutional Neural Network (CNN)-based technique a...

Prediction of work Travel mode choice is one the most important parts travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In prediction selection, machine learning methods are commonly employed. To fit a machine-learning model various challenges, hyperparameters must be tweaked. Choosing optimal hyperparameter configuration for models has an immediate effect on performance model. this paper, optimizing common...

Bisham Qilla and Doyian stations, Indus River Basin of Pakistan Water pollution is an international concern that impedes human health, ecological sustainability, agricultural output. This study focuses on the distinguishing characteristics evolutionary ensemble machine learning (ML) based modeling to provide in-depth insight escalating water quality problems. The 360 temporal readings electric conductivity (EC) total dissolved solids (TDS) with several input variables are used establish...

Co-designing energy systems across multiple carriers is increasingly attracting attention of researchers and policy makers, since it a prominent means increasing the overall efficiency sector. Special attributed to so-called hubs, i.e., clusters communities featuring electricity, gas, heat, hydrogen, also water generation consumption facilities. Managing an hub entails dealing with sources uncertainty, such as renewable generation, demands, wholesale market prices, etc. Such uncertainties...

Brushless DC motors play a vital role as workhorse in many applications, especially home appliances. In the competitive world of day, brushless motor is wise choice for applications because its high power density, simple driving circuit, and efficiency. Accordingly, demonstrating feasibility new controller on this type has undoubtedly paramount importance. Two methods speed controllers, namely linear-quadratic regulator, proportional-integral-derivative, are mixed using mixture experts (MoE)...

This paper analyzes the technical and economic possibilities of integrating distributed energy resources (DERs) energy-storage systems (ESSs) into a virtual power plant (VPP) operating them as single plant. The purpose study is to assess efficiency VPP model, which influenced by several factors such price production. Ten scenarios for were prepared on basis installed capacities hydropower (HPP), rooftop solar photovoltaic (PV), system (ESS), well weather conditions, in Poland. On it was...

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting from the by peak regardless of speed. The proposed MPPT implements ANFIS approach with a backpropagation algorithm. rotor speed acts as input to and torque reference controller’s output, which further inputs side converter’s control loop rotor’s actual...

Electric vehicle aggregator (EVAGG) is an independent entity that facilitates exchanging electricity between electric vehicles (EVs) and the grid. Energy hub (EH) another playing a remarkable role in enhancing efficiency, flexibility, reliability of multi-energy systems. Although interacting various agents beneficial to enhance their capability, it challenging schedule such interconnected entities. In this paper, EVAGG EH, as entities, are scheduled independently only exchange information...

The article presents calculations and power flow of a real virtual plant (VPP), containing fragment low medium voltage distribution network. VPP contains hydropower (HPP), photovoltaic system (PV) energy storage (ESS). purpose this is to summarize the requirements for connection generating units grid. Paper discusses impact on maximum installed capacity distributed resource (DER) systems parameters unit. Firstly, comprehensive review definitions, aims, as well characteristics investigated...

Distribution transformer is the most vital component in power system. Failure of a leads to loss revenue besides affecting reliability supply consumers. It can lead non-availability for long duration. Due this, it important maintain good quality mineral oil. Thus, if oil reduced then its dielectric strength/quality degraded. Finally, affect services transformer, terms continuity supply. This paper entails development mathematical MATLAB/Simulink model which able calculate life cycle...

Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various types. On other hand, have usually been exposed uncertain environments due presence of renewable sources (RESs) interaction with electricity markets. Hence, this paper develops novel optimization framework based on hybrid information gap decision theory (IGDT) robust (RO) handle optimal self-scheduling EH within...