Harris Hawk Research Articles

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Dynamic random mutation hybrid Harris hawk optimization and its application to training kernel extreme learning machine

Dynamic random mutation hybrid Harris hawk optimization and its application to training kernel extreme learning machine

A Manta-Ray Hill Climbing Vision Transformer Model for Predicting Ischemic Stroke Outcome

An ischemic stroke attack can cause permanent damage to healthy brain tissue, leading to a permanent loss of motor or sensory function. It can also result in disability or death if not diagnosed and treated promptly. Early prediction of the outcome of the first stroke, such as disability or death, can help many patients by administering appropriate medications to save their lives. Additionally, early prediction of a recurrent stroke within 14 days of the initial stroke can contribute to prevent its recurrence. This paper first proposes a modified Manta-Ray Foraging Optimizer (MMRFO) to enhance the characteristics of the MRFO technique. This approach is based on incorporating the Hill Climbing methodology into the original MRFO in order to improve the exploitation phase, which is responsible for locating the promising zone in the search area. The proposed approach is then utilized to determine the appropriate hyperparameters of the Vision Transformer(ViT) model to predict stroke outcomes prior to its occurrence. To transform categorical data to numerical values, an ASCII encoder module is included. In the feature selection step, the Harris Hawk Optimization approach (HHO) is used to identify the most important elements that may define the stroke. A comparative study has been performed to confirm the effectiveness of the proposed methodology. The results demonstrate that the proposed technique with a Vision Transformer achieves superior results compared to state-of-the-art algorithms. The accuracy of the proposed technique was improved to 87% for the first dataset and 83% for the second, which is clearly superior to that of the other models and earlier research.

Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection of the best-fit model depends on finding an excellent connection between inputs and output(s) layers in training and testing datasets (e.g., a combination of expert knowledge, experimentation, and validation techniques). The study's primary result is a division of the participants into two performance-based groups (failed and non-failed). The experimental data used to build the models measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, and admission grade. During the model evaluation, a scoring system was created in addition to using mean absolute error (MAE), mean squared error (MSE), and area under the curve (AUC) to assess the efficacy of the utilized approaches. Further research revealed that the HHO-ANFIS is superior to the EWA-ANFIS. With AUC = 0.8004 and 0.7886, MSE of 0.62689 and 0.65598, and MAE of 0.64105 and 0.65746, the failure of the pupils was assessed with the most significant degree of accuracy. The MSE, MAE, and AUC precision indicators showed that the EWA-ANFIS is less accurate, having MSE amounts of 0.71543 and 0.71776, MAE amounts of 0.70819 and 0.71518, and AUC amounts of 0.7565 and 0.758. It was found that the optimization algorithms have a high ability to increase the accuracy and performance of the conventional ANFIS model in predicting students' performance, which can cause changes in the management of the educational system and improve the quality of academic programs.

Optimized low carbon scheduling strategy of integrated energy sources considering load aggregators

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

OTFS narrowband interference suppression based on energy concentration

Orthogonal Time Frequency Space (OTFS) modulation has significant Doppler resistance in non-geostationary orbit (NGSO) satellite communications, but is extremely affected by single-tone interference (STI). In this paper, a Fast Harris Hawk Optimization-based Energy Concentration Interference Suppression (FHHO-ECIS) algorithm is proposed to remove STI. First, the algorithm designs the translation matrices in the Delay-Doppler (DD) domain based on the interference center. The STI energy is concentrated by the translation matrices to a single point in the OTFS data block and removed by a zero-setting operation. Subsequently, an interference center pre-estimation module is proposed to improve the accuracy and speed of the HHO algorithm for interference center estimation. Finally, the theoretical analysis of the algorithm's interference suppression gain and the effect of the zero-setting operation on the data is performed. Simulation results show that the FHHO-ECIS algorithm improves the signal to interference plus noise ratio (SINR) by more than 1.7 dB and the bit error rate (BER) performance is improved by more than 0.3 dB.

Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China

Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making.

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress–strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning

Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzău river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance ≈ 20%), distance from river (importance ≈ 17.5%), land use (importance ≈ 12%) and TPI (importance ≈ 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35–40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One Dimensional-Convolutional Neural Network.

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization-slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization-slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization-slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images

Background and Objective One of the illnesses with most significant mortality and morbidity rates worldwide is lung cancer. From CT images, automatic lung tumor segmentation is significantly essential. However, segmentation has several difficulties, such as different sizes, variable shapes, and complex surrounding tissues. Therefore, a novel enhanced combined intelligent system is presented to predict lung cancer in this research. Methods Non-small cell lung cancer should be recognized for detecting lung cancer. In the pre-processing stage, the noise in the CT images is eliminated by using an average filter and adaptive median filter, and histogram equalization is used to enhance the filtered images to enhance the lung image quality in the proposed model. The adapted deep belief network (ADBN) is used to segment the affected region with the help of network layers from the noise-removed lung CT image. Two cascaded RBMs are used for the segmentation process in the structure of ADBN, including Bernoulli–Bernoulli (BB) and Gaussian-Bernoulli (GB), and then relevant significant features are extracted. The hybrid spiral optimization intelligent-generalized rough set (SOI-GRS) approach is used to select compelling features of the CT image. Then, an optimized light gradient boosting machine (LightGBM) model using the Ensemble Harris hawk optimization (EHHO) algorithm is used for lung cancer classification. Results LUNA 16, the Kaggle Data Science Bowl (KDSB), the Cancer Imaging Archive (CIA), and local datasets are used to train and test the proposed approach. Python and several well-known modules, including TensorFlow and Scikit-Learn, are used for the extensive experiment analysis. The proposed research accurately spot people with lung cancer according to the results. The method produced the least classification error possible while maintaining 99.87% accuracy. Conclusion The integrated intelligent system (ADBN-Optimized LightGBM) gives the best results among all input prediction models, taking performance criteria into account and boosting the system’s effectiveness, hence enabling better lung cancer patient diagnosis by physicians and radiologists.

CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification

Abstract Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.

Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn't work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification.

Mining the colossal patterns using ISSA based KMC with VGHHO clustering model for high dimensional data

High-dimensional datasets comprise a few rows but a huge total of features. The increased difficulty data scientists have in efficiently gleaning insights from high-dimensional large data is a direct outcome of this trend. Therefore, a K-Means clustering method (KMC) must be implemented to cleanse this data, with the centroid of KMC being ideally chosen by improved squirrel search algorithm (ISSA). The suggested algorithm features not one but two different types of searches: the leaping search and the progressive search. The linear regression selection strategy is utilised to automatically choose the applicable approach during the evolutionary phase; this increases SSA's stability. Harris Hawks Optimizer (HHO), which replicates the behaviour of a Harris hawk during rabbit predation, is applied to the cleaned data to cluster the massive pattern. However, HHO has problems with low accuracy and early convergence because of its inability to strike a good balance among exploitation. A new variant of HHO, dubbed velocity-guided HHO (VGHHO), including three improvements is proposed to address these drawbacks. By including a velocity operator and an inertia weight into the search equation, we are able to create a unique modified position search equation for use during the exploitation phase. Then, we incorporate a learning mechanism based on refraction and opposition to provide the promising resolutions and aid the swarm in escaping the local optimal solution. After the massive cluster has been built with VGHHO, uproot technology is used to uncover massive patterns. We run the tests on a wide variety of high-dimensional datasets and employ a number of different efficiency metrics. Evidence from these investigations shows that the proposed method produces high-quality mining results.

Optimal gene therapy network: Enhancing cancer classification through advanced AI-driven gene expression analysis

Gene therapy is an advanced medical approach that aims to find solutions for various cancers by identifying optimal gene expressions. In this context, computer-aided detection of gene expressions becomes a research challenge, where artificial intelligence methods are employed to classify cancer types. However, traditional machine learning models must be improved for accurately classifying cancers, leading to unsatisfactory quantitative performance. Therefore, this work implemented the optimal gene therapy network (OGT-Net) for identifying the different types of cancers from the gene expression sequences. Initially, the dataset pre-processing operation normalizes the dataset, which maintains the uniform nature of all records in the dataset. Then, the light gradient boosting model (LGBM) extracts the correlated features from the pre-processed dataset, which contains the relationship among the pre-processed gene expression data. In addition, interrupt-based Harris Hawk optimization (IHHO) extracts the optimal features from LGBM data, decreasing the total number of features by removing redundant gene sequences. Then, a customized deep learning convolution neural network (DLCNN) is used to categorize diseases using gene expression datasets based on lymphography, colon, lung, ovarian, and prostate cancers. The simulation results reveal that the proposed OGT-Net improved performance on various datasets compared to existing approaches, with an average accuracy of 91.128 %, precision of 90.836 %, recall of 91.25 %, and F1-score of 90.7 %.

Enhancing Fault Detection and Classification in MMC-HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk’s optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system’s effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability.

Modified Harris Hawk algorithm-based optimal photovoltaics for Voltage Stability and Load Flow Analysis

ABSTRACT In the distribution system, proper placement of distributed generation (DG) is still a very challenging issue for getting their maximum potential benefits. Power system stability should be determined through load modeling, hybrid energy storage system (HESS), and photovoltaics (PV) modeling. This paper proposed the optimal sizing and location of photovoltaic using the Modified Harris Hawk Optimization (MHHO) algorithm. The energy generated from the photovoltaic (PV) is stored in the HESS, where the energy’s charging and discharging from the storage unit is framed with a reduced power loss (active and reactive power). The optimal sizing and placement of the PV system will provide better voltage stability. The Modified Harris Hawk Optimization Algorithm (MHHO) frames the system’s voltage stability. The Newton-Raphson load flow method is used to analyze the loading in the power system. The IEEE 33 and 69 bus systems are used to analyze the load flow to the consumers, thus analyzing the power system’s dynamic performance. The performance and voltage stability of the presented model are compared with the existing optimization techniques, which include the Firefly Algorithm (FA), Crow Search Optimization (CSO), Particle Swarm Optimization (PSO), Analytical PSO (APSO), Improved Cayote Optimization Algorithm (COA) and Whale optimization algorithm (WOA) through the MATLAB/Simulink platform.

A novel deep learning and Internet of Things (IoT) enabled precision agricultural framework for crop yield production

<p>Precision agriculture is a growing concept that frequently refers to enhancing farms via the use of up-to-date knowledge and cutting-edge technology, which in turn aids farmers by automating and improving them to increase rural profitability. This paper suggests the novel framework Deep-Plant-IoT which amalgamates the Internet of Things (IoT) and Deep learning framework for an effective prediction of crop yields which act as intelligent recommendation systems that can significantly improve the production. The framework incorporates IoT sensors and devices to collect and store the soil parameters in the cloud. Then these data are downloaded offline and the Harris Hawk Optimized Long Short Term Memory network is deployed to effectively predict crop yields that can aid in better production. Nearly 15902 data were collected for two months and Extensive testing was undertaken to employ these data to evaluate and analyze the proposed framework. Moreover, the prediction algorithm proposed in the framework is evaluated in comparison to other cutting-edge learning models. The suggested algorithm has demonstrated greater performance such that 98% accuracy, 97.23% precision, 97.0% recall, and 97.2% F1-score respectively.</p>