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One of the main determinations of India is agriculture and the quantity of production is very essential in the field of cultivation. The rice crops are crucially admitted as one of the powerful energy streams for the production of resources. But rice plants suffer from various diseases due to infections that occur in leaf, fruit and stem. Hence, the production of rice plant is decreased and reflects severe issues in economic, agriculture and communal loss. The usage of image processing in leaf disease detection plays a vital role to diagnose and control it effectively. Though there are many existing works are done too detect rice plant leaf disease, still the presence of vagueness, impreciseness due to noise and outliers are primary challenges. The main aim of this paper is to optimize rice plant leaf disease detection by developing neutrosophic C-means clustering for segmentation. The features are extracted and their similarity is evaluated using neutrosophic Hausdorff distance. The degree of similarity is done based on truthiness, indeterminacy and falsity. The simulation results proved that enhance neutrosophic C-means clustering achieves highest rate of accuracy in rice plant leaf disease detection with less error rate compared to K-means, DBSCAN and FCM

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

Sensor reduction is a key sought after requirement for a surface mounted permanent magnet synchronous motor (SPMSM) electric vehicle (EV) drive to improve reliability and to reduce its cost. However, achieving a smooth and successful wide speed range using sensorless control is still a challenge. This article presents a modified higher order sliding mode observer (SMO)-based position sensorless predictive current control for wide range speed operation of an SPMSM-driven light EV. For reducing chattering and singularity phenomenon in conventional SMO, a modified speed adaptive super twisting SMO with moving average filter phase-locked loop (PLL) is deployed to accurately estimate the position and speed of SPMSM. Moreover, optimal switching pulses are provided to a voltage source inverter using time delay-compensated finite control set-model predictive current controller, eliminating the need of proportional integral controllers and modulators. Simulated performance of the control scheme is presented in MATLAB Simulink and its hardware implementation is executed using an SPMSM EV drive laboratory test setup. A comprehensive study of different PLL-based SMOs is carried out and the effectiveness of proposed system against conventional and other methods is demonstrated experimentally. It is observed that this method allows seamless sensorless operation at 3.95% of rated speed.

In the present work, Aspergillus niger was employed to produce commercially valuable malic acid from crude glycerol derived from waste cooking oil. Crude glycerol dosage, yeast extract dosage and initial pH were the influencing factors playing a significant role in the malic acid synthesis. The optimal condition for malic acid biosynthesis was studied by using response surface methodology. Further the feasibility analysis for biosynthesis of malic acid from crude glycerol was studied using the laboratory scale optimized data, with this experimentally optimized data, plant was simulated using SuperPro Designer (v10). The cost involved for malic acid synthesis per unit volume was likely expected to be $0.43/kg of malic acid using reactive extraction method. Thus, process optimization combined with techno-economical analysis of malic acid production could be beneficial.

This is a nature poem set against the backdrop of a couple sitting near a riverside.

This paper emphasizes a 3-∅, LCLC type, 1-stage PV system unified to the grid. The proposed system not only transfers solar energy to load but also mitigates pq issues like harmonics, grid current unbalancing and reactive power. A comparison of SRF theory, \(\mathrm{I}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{\varnothing }\) algorithm and improved adaptive linear sinusoidal tracer (ALST) algorithm is presented in the proposed LCLC type distributed static compensator fed by a photovoltaic array (PV DSTATCOM). A MPPT algorithm is used to preserve the voltage between the dc link capacitor terminals. Using MATLAB and Simulink and by utilizing simscape toolbox the proposed PV DSTATCOM is simulated. The comparison of SRF theory, \(\mathrm{I}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{\varnothing }\) and improved ALST algorithm has been done to show the effectiveness of the latter without having complex calculations as compared to other adaptive controllers like fuzzy logic and neural network. The total harmonic distortions of source current and point of common coupling voltage are measured within IEEE-929 and IEEE-519 standards.

The subject of the paper is concerned with the simulation and conception of a DSTATCOM using an Artificial Neural Network (ANN) algorithm for controlling an IGBT- based Voltage Source Converter (VSC) for a 3∅-3wire system. The system feeds different loads through the controller, which is accustomed to mitigating power quality complications such as harmonics, unbalance in grid currents, and reactive power. Conventional regulators/control algorithms being fixed design gives ideal execution or result just over a restricted scope of working conditions for which they are planned. Alternative regulators are dependent on neural networks and fuzzy logic, which are sturdier and can be intended to work well under a broad scope of operating conditions. The results of Improved Linear Sinusoidal Tracer (ILST), and Artificial Neural Network (ANN) is presented to show the efficacy of later by simulating the system in MATLAB software using Simscape blockset and their comparison is conferred. The total harmonic distortions of source current and source voltage are estimated which are under IEEE-929 and IEEE-519 guidelines.

This paper presents an efficient sensorless permanent magnet synchronous motor (PMSM) drive based on model reference adaptive system (MRAS) speed and position estimation for solar photovoltaic (PV)- battery power driven electric vehicle (EV). The aim of this work is to include a technique that encourages renewable energy applications. This work involves regenerative braking to increase the range of EV. The kinetic energy of the vehicle is recovered during braking conditions and utilized to charge the battery as well as to avoid deep discharge of the battery during the uphill driving condition. In order to reduce cost, weight, and increase robustness of the drive, MRAS based sensorless control is used for speed and position estimation. In this method, a current adaptable model is analyzed in which, PMSM itself acts as a reference model. A solar PV array integrated with a battery provides a seamless and continuing operation independent of environmental conditions. A Cuk DC-DC converter is utilized for maximum power point tracking (MPPT), which ensures continuous input and output currents. This system is efficient, cost-effective, and suits for practical implementation. A study of the PMSM control and the effectiveness of the drive system simulated using MATLAB simulink is presented in this paper.

For the quick and accurate dynamic response of a sensorless drive system, proper estimation of position and speed is indispensable. A conventional sliding mode observer (SMO) based sensorless control has several limitations like undesired frequency swing during phase jump, spurious noise speed reversal issue. This paper presents an improved SMO based sensorless control and finite control set model predictive control (FCS-MPC) of permanent magnet synchronous motor (PMSM) EV drive with regenerative braking. An improved phase locked loop (PLL) with a modified control function is utilized to obtain position and speed. It provides a smooth startup and avoids transient in frequency and singularities issues. To avoid cascading linear controllers, a robust FCS-MPC is employed in this paper. It provides switching pulses directly to the switches of voltage source inverter (VSI), which avoids the use of modulators. The proposed sensorless control method for an electric vehicle (EV) application is simulated using Simulink and the effectiveness and compatibility of this control algorithm is verified.

Major impediments towards effectuating electric vehicles (EVs) on the road, are cost, range anxiety and the battery re-charge time. In this work, a PMSM (Permanent Magnet Synchronous Motor) driven solar PV-battery based three-wheeler EV with regenerative braking feature is designed and investigated to address these issues. In addition, a model reference adaptive control-based position sensorless operation is successfully incorporated into the drive to improve reliability and to reduce the cost. A detailed design and selection of the solar PV array and power converters for the system topology are introduced in this paper. The steady state and dynamic operation of the system configuration is studied in MATLAB/ Simulink and test results demonstrating the sensorless vector control are presented here. This simple topology improves the range, efficiency and longevity of a three-wheeler EVs.