A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts

Meta Learning-Based Dynamic Ensemble Model for Crop Selection

Agricultural sector is working for optimal crop yield toward securing a sustainable food supply for the world. Fast growth in precision agriculture helps farmers to increase their yields by extending the era of machine-learning techniques. However, in organic and inorganic farming, predicting yield is an open issue that dominantly depends on the presence of soil nutrients. The lack of knowledge about the richness of land nutrients deals with the crop selection problem. Therefore, the proposed work extended the idea of a dynamic ensemble model for imbalanced multi-class nutrient data. In this work, an attempt is being made to include a novel customized voting strategy for deciding the final class output from the ensemble model. As an initial step, a well-known ranking technique, VIKOR, is applied over land nutrients to extract the most informative land samples. The rationale is to reduce the complexity of the ensemble model by determining only informative land samples for further classification. Furthermore, the meta-learning approach of dynamic ensemble selection accounts for multi-criterion-based competent classifier selection as meta-classifiers. These meta-classifiers decide on ensemble formation with the customized voting strategy to classify the right crop for the test land. To investigate nutrient richness, real-time soil and water nutrient data are collected from the soil testing laboratory, which covers different spatial data. Our experiments on six popular DES algorithms over nutrient data reveal the proposed algorithm’s outperformance in specificity, sensitivity, BCA, Multi-Area under Curve, and precision. Moreover, the lesser computational time of the proposed work indicates the model’s efficiency toward suitable crop selection

A Review on Hydrogen-Based Hybrid Microgrid System: Topologies for Hydrogen Energy Storage, Integration, and Energy Management with Solar and Wind Energy

Hydrogen is acknowledged as a potential and appealing energy carrier for decarbonizing the sectors that contribute to global warming, such as power generation, industries, and transportation. Many people are interested in employing low-carbon sources of energy to produce hydrogen by using water electrolysis. Additionally, the intermittency of renewable energy supplies, such as wind and solar, makes electricity generation less predictable, potentially leading to power network incompatibilities. Hence, hydrogen generation and storage can offer a solution by enhancing system flexibility. Hydrogen saved as compressed gas could be turned back into energy or utilized as a feedstock for manufacturing, building heating, and automobile fuel. This work identified many hydrogen production strategies, storage methods, and energy management strategies in the hybrid microgrid (HMG). This paper discusses a case study of a HMG system that uses hydrogen as one of the main energy sources together with a solar panel and wind turbine (WT). The bidirectional AC-DC converter (BAC) is designed for HMGs to maintain power and voltage balance between the DC and AC grids. This study offers a control approach based on an analysis of the BAC’s main circuit that not only accomplishes the function of bidirectional power conversion, but also facilitates smooth renewable energy integration. While implementing the hydrogen-based HMG, the developed control technique reduces the reactive power in linear and non-linear (NL) loads by 90.3% and 89.4%

Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems

Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods

Intelligent RBF-Fuzzy Controller Based Non-Isolated DC-DC Multi-Port Converter for Renewable Energy Applications

In this article, a multi-port non-isolated converter is implemented for renewable energy applications. High voltage gain is accomplished with a switched capacitor and coupled inductor, and power transfer between the inputs, battery, and load can be realized using three power switches. The power collected in the leakage inductance is reused to decrease the voltage stress on the power switch. Various functioning periods are also examined, and design requirements are offered. The proposed converter uses fewer parts to realize power flows and obtain high voltage gain compared to comparable converters. Additionally, under partial shading conditions, the traditional maximum power point tracking (MPPT) approaches are not able to collect the global maximum power point (MPP) from the numerous local MPPs. This work proposes an artificial neural-network-based MPPT technique with variable step size for tracing speed, MPP oscillations, and operating efficiency. The proposed converter experiment is also constructed and successfully tested in a laboratory environment

Design and Implementation of SAE J1939 and Modbus Communication Protocols for Electric Vehicle

The charging station has a vital role in the electric vehicle sector. The charging station provides supply (AC or DC) to vehicles as per requirements. The charging station infrastructure includes software and hardware that ensure energy transfer and safety. Communication is mandatory to transmit messages that contain information from the battery management system and charger. This research focuses on implementing the communication between the charger controller and the battery management system. This paper adopts the controller area network (CAN) bus charger communication protocol based on the SAE J1939 standard from the Society of Automotive Engineers. The data are transmitted over a network to facilitate the information that is to be conveyed by an electronic control unit. The vehicle communicates via the battery management system to the charger controller using CAN communication. The charger power modules with AC to DC and DC to DC converters uses Modbus communication protocol. Therefore, this paper integrates CAN bus and Modbus communication protocols and implements the communication between charger and electric vehicle battery management system using a cost-effective Arduino UNO micro-controller. Using the CAN bus module (MCP2515) and Modbus module (MAX485), the distance between the electric vehicle and the charger is increased. Finally, the communication is validated using PCAN View software

Real-Time Automatic Investigation of Indian Roadway Animals by 3D Reconstruction Detection Using Deep Learning for R-3D-YOLOv3 Image Classification and Filtering

Statistical reports say that, from 2011 to 2021, more than 11,915 stray animals, such as cats, dogs, goats, cows, etc., and wild animals were wounded in road accidents. Most of the accidents occurred due to negligence and doziness of drivers. These issues can be handled brilliantly using stray and wild animals-vehicle interaction and the pedestrians’ awareness. This paper briefs a detailed forum on GPU-based embedded systems and ODT real-time applications. ML trains machines to recognize images more accurately than humans. This provides a unique and real-time solution using deep-learning real 3D motion-based YOLOv3 (DL-R-3D-YOLOv3) ODT of images on mobility. Besides, it discovers methods for multiple views of flexible objects using 3D reconstruction, especially for stray and wild animals. Computer vision-based IoT devices are also besieged by this DL-R-3D-YOLOv3 model. It seeks solutions by forecasting image filters to find object properties and semantics for object recognition methods leading to closed-loop ODT

Analyzing the Electronics of Image Sensors and Their Functionality to Develop Low Light-Emitting Source Image

Bioluminescence imaging has been used to visualize the biological effects of human beings and is a promising technique in a recent modality. In this study, the digital image technique is used to improve quality and recover images. The optical fluence that emerges from the source is generated using a camera, and a low resgolution is observed. In this paper, the diurnal change of ultra-weak photon emission was successfully imaged with an improved, highly sensitive imaging system using a charge-coupled device (CCD) camera. The changes in energy metabolism might be linked with diurnal changes in photon emission, and when observed, the body emits extremely weak light spontaneously without external photoexcitation. Therefore, to obtain accurate information, a combined Barn Door Star Tracker approach has been proposed to improve the accuracy of the method and has been implemented to test on celestial bodies. The ability to temporally assess the location of star movement can be monitored accurately with bioluminescence imaging