Search

This paper simulates the lead-free or non-toxic PSC (perovskite solar cell) based on cesium tin germanium halide (CsSnGeI3). The effect of different electron transport layers is understood by mutually related the built-in potential (Vbi) with the open circuit voltage (VOC). The simulation study reveals that electron transport layer (ETL) shows a crucial effect on the photovoltaic performance (PV performance) of solar cell. It is obtained from simulation, that ZnOS (Zincoxysulfide) as an ETL shows better device efficiency. Further, the PV performance is optimized based on the defect density (DD) of perovskite absorber layer. The proposed work depicts that at the optimum defect density of 1 × 1014 cm−3 a remarkable device efficiency is achieved i.e., ~ 25.43%.

Recent advancement in smart phones and computing technologies has played a vital role in people’s life. Develop a model to detect the human basic dynamic activities such as Amble, Climb stairs, coming down the stairs into the floor and human basic static activities like Sitting, Standing or Laying using the person’s smart phone and computers are the major work of this paper. Conventional Machine learning models like Logistic Regression, SVC, Decision tree, etc. results are compared with a recurrent deep neural network model named as Long Short Term Memory (LSTM). LSTM is proposed to detect the human behavior based on Human Activity Recognition (HAR) dataset. The data is monitored and recorded with the aid of sensors like accelerometer and Gyroscope in the user smart phone. HAR dataset is collected from 30 persons, performing different activities with a smart phone to their waists. The testing of the model is evaluated with respect to accuracy and efficiency. The designed activity recognition system can be manipulated in other activities like predicting abnormal human actions, disease by human actions, etc. The overall accuracy has improved to 95.40%.

In recent years, weeds are responsible for agricultural losses. To get rid of this problem, the farmers have to uniformly spray the whole field with the weedicides which require a huge quantity of weedicides. The process of spraying weedicides affects the environment. Weed detection in dense culture is a plant science problem that is important for field robotics where the detection of weed is currently a challenge so that the use of phytochemical products on crops can be reduced. To control and prevent specific weeds, a method of detecting the weed is presented in this paper. By collecting the plants and weeds datasets which are grayscale images, data is divided into training, validation, and testing datasets and then transported to the convolutional neural network. Based on the knowledge gained by the model, it can detect the weeds among plants. Utilization of a pre-trained VGG16 model for weed detection in dense cultures demonstrated improved performance compared to state of the art without the need for large datasets and high computational power for training.

In the presented work, the authors intend to detect and classify disease in cherry plants at a premature stage by analyzing its leaves. For experimental purposes, PlantVillage dataset has been used. Several machine learning models and a pre-trained CNN model have also been implemented for performance analysis. The performance analysis uses various metrics for evaluation like the number of epochs, AUC-ROC curve, recall, precision, and several other parameters. The proposed model when applied, the experimental results gave a better accuracy than the conventional ML algorithms. The implemented pre-trained model has achieved an approximate accuracy of about 99.89%.

Vehicular Ad Hoc Network (VANET) has been emerged out as one of the most popular areas of research which gathers the attention of the research community. VANET is basically deployed to integrate the responsiveness of widely active wireless-based networks to the vehicles. The basic idea here is to provide the abundant connectivity to the vehicles either with the help of efficient vehicle-to-vehicle or vehicle-to-infrastructure communication link which permits the adoption of Intelligent Transportation Systems (ITS). In order to design and develop a right and capable routing protocol for the VANET, a rigorous study of popular existing VANET routing protocols is always required. In this work, some of the most widely used routing protocols like AODV, AOMDV, DSR, DSDV are taken into the consideration for the comparison purpose in terms of routing performance based on a set of parameters chosen. The purpose of this work is to explore the simulation of two real-world-scenarios of wireless systems in VANET. The routing protocols are compared with the help of network simulator—2 (NS 2), MOVE, SUMO, etc. The evaluation metrics used for the comparison purpose include PDR (Packet Delivery Ratio) and NRL (Normalized Routing Overhead). The outcome of simulations suggests that AODV and AOMDV are much suitable out of the four protocols for the real-time scenario which was taken into the consideration.

Potato is one of the most cultivated and in-demand crops after rice and wheat. Potato farming dominates as an occupation in the agriculture domain in more than 125 countries. However, even these crops are, subjected to infections and diseases, mostly categorized into two grades: (i) Early blight and (ii) Late blight. Moreover, these diseases lead to damage the crop and decreases its production. In this paper, we propose a deep learning-based approach to detect the early and late blight diseases in potato by analyzing the visual interpretation of the leaf of several potato crops. The experimental results demonstrate the efficiency of the proposed model even under adverse situations such as variable backgrounds, varying image sizes, spatial differentiation, a high-frequency variation of grades of illumination, and real scene images. In the proposed Convolution Neural Network Architecture (CNN), there are four convolution layers with 32, 16, and 8 filters in each respective layer. The training accuracy of the proposed model is obtained to be 99.47% and testing accuracy is 99.8%.

In this paper, a deep learning-based approach has been developed to classify the images of galaxies into three major categories, namely, elliptical, spiral, and irregular. The classifier successfully classified the images with an accuracy of 97.3958%, which outperformed conventional classifiers like Support Vector Machine and Naive Bayes. The convolutional neural network architecture involves one input convolution layer having 16 filters, followed by 4 hidden layers, 1 penultimate dense layer, and an output Softmax layer. The model was trained on 4614 images for 200 epochs using NVIDIA-DGX-1 Tesla-V100 Supercomputer machine and was subsequently tested on new images to evaluate its robustness and accuracy.

Tuberculosis is an ancient disease which has become rampant in recent times due to it’s multidrug resistant nature. Global community is concerned about it and measures to control it are being taken in the form of Millennium Development Goals and Stop TB Strategy. These goals can be achieved by early case finding and better diagnosis. Detection of latent TB infection and it’s proper treatment is also necessary to eliminate disease. A newer vaccine which could either replace or accentuate the current BCG vaccine is also demand of the time. And the last step in the direction of elimination of TB will be judicious use of currently available drugs. It is also necessary that we come out with newer anti-TB drugs and regimens which could handle the issues like cost and toxicity. In India Revised National TB Program (RNTCP) has also implemented National Strategic Plan to eliminate TB by 2030.

CNG is considered a low maintenance cost and environment friendly fuel. Its use as an alternative fuel has surged in cities having CNG stations. Due to limited number of CNG stations, there is a substantial gap between demand and supply of CNG fuel. CNG dispensing unit is an important system of CNG station. Extended operation of dispensing unit is required for delineating this gap. For this, availability and reliability of CNG dispensing unit should be high. The present study reviews and exemplifies the fuzzy reliability analysis approach for behavioural analysis of CNG dispensing unit. The reliability block diagram and fuzzy Lambda-Tau approach have been used for evaluating reliability parameters. Fuzzy methodology has been used for representing failure rate and repair time. In present research work a comparative study of conventional fuzzy theory and vague theory has been expounded. The crisp reliability input and output data have been fuzzified using extension principle and alpha-cut approach. The fuzzy output has been defuzzified for assessing the system behaviour. The results of the study were communicated to system analyst and maintenance engineer.

In a CNG refueling station, identification of the risks associated with the complex operating systems and its prioritization is essential. This identification and prioritization will result in a proper understanding of the system by reliability analyst and maintenance engineer so as to frame appropriate maintenance planning. A fuzzy rule-based inference model taking into account the failure modes for risk ranking in FMEA to manage risks and make maintenance decisions is applied to a CNG station in this paper. One of the methods used for risk analysis is FMEA (Failure Mode and Effect Analysis) where we determine an RPN (Risk Priority Number) by multiplying the feature scores that are obtained from the degree of probability of occurrence (O), severity (S) and detection (D) without taking into consideration the relative importance of the factors. In the fuzzy approach, we use a linguistic variable for calculation of RPN. This method is so preferred because it provides us with unbiased judgment. The results obtained from this study show that the ambiguity in the conventional FMEA can be solved with a fuzzy approach, and conveniently discover potential failure modes and help in risk and reliability analysis of the system.

Data warehouse is the most important asset of an organization as it contains highly valuable and sensitive information that is useful in decision-making process. The data warehouse provides easy access to organizational data as it contains data from different sources. Thus, it is essential to structure security measures for the protection of data that resides in data warehouse against malicious attackers to ensure proper security and confidentiality. Security should be considered vital from initial stages of designing data warehouse and hence should be deployed. Though a lot of work is being done towards the improvement and development of data warehouse till now but very less attention is given on the implementation of the security approaches in data warehouse. This paper focuses on improving the level of security by combining One-Time Pad (OTP) encryption technique with Advanced Encryption Standard (AES) to encrypt the data before loading it into data warehouse. Finally, the proposal of the model is to incorporate OTP encryption technique in the architecture of data warehouse for enhancing its security.

Cloud computing is one of the most acceptable emerging technologies, which involves the allocation and de-allocation of the computing resources using the Internet as the core technology to compute the tasks or jobs submitted by the users. Task scheduling is one of the fundamental issues in cloud computing and lots of efforts have been made to solve this problem. For the success of any cloud-based computing model, efficient task scheduling mechanism is always needed which, in turn, is responsible for the allocation of tasks to the available processing machines in such a manner that no machine is over- or under-utilized while executing them. Scheduling of tasks belongs to the category of NP-Hard problem. Through this paper, we are proposing the particle swarm optimization (PSO)-based task scheduling mechanism for the efficient distribution of the task among the virtual machines (VMs) in order to keep the overall response time minimum. The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism and results clearly shows that the PSO-based task scheduling mechanism clearly outperforms the others techniques which are taken into consideration.

Cloud computing fulfills the demand of the people, to use the computing as a tool or utility by providing the elastic, cost-effective, easy to maintain computing resources over the Internet across the world. As a result, more and more devices connecting themselves using the Internet and enabling web technologies to access such computing utilities require more and more processing and storage equipment’s. Such response in the rate of adoption of cloud-based application led to the massive growth in size of data or we can say it “big data”. This work is going to focuses on the data management aspects of cloud computing as traditional relational database management principles are facing problems in managing and handling the big data. Through this paper, we are also going to discuss the definition, characteristics, relationship of big data with cloud computing, and open issues in the field.

Recently, Cloud computing emerges out as a latest technology which enables an organization to use the computing resources like hardware, applications, and software, etc., to perform the computation over the internet. Cloud computing gain so much attention because of advance technology, availability, and cost reduction. Task scheduling in cloud computing emerges out as new area of research which attracts the attention of lots researchers. An effective task scheduling is always required for optimum or efficient utilization of the computing resources to avoid the situation of over or under-utilization of such resources. Through this paper, we are going to proposed the cuckoo search-based task scheduling approach which helps in distributing the tasks efficiently among the available virtual machines (VM’s) and also keeps the overall response time (QoS) minimum. This algorithm assigns the tasks among the virtual machines on the basis of their processing power, i.e., million instructions per seconds (MIPS) and length of the tasks. A comparison of cuckoo search algorithm is done with the first—in first—out (FIFO) and greedy-based scheduling algorithm which is performed using the CloudSim simulator, the results clearly shows that cuckoo search outperforms the other algorithms.