DGs have become increasingly important to the grid in connection with renewable energy systems as fossil fuels run out and pollution is on the rise. The integration of renewable energy sources into low-and medium-voltage distribution networks has opened up a new field of study. To ensure that distributed generation (DG) connections to the grid meet power quality standards, electronic power converters are a common component of renewable energy systems. In this case, major power quality issues will always occur if the inverters' switching frequencies are not appropriately location. On the other hand, power quality reductions can cause acute problems inpower networks, such as decreased performance, decreased useful life and efficiency of electrical and electronic equipment in the network, and series and parallel resonance caused by inductors and capacitors in some harmonics, which corrupts the distribution voltage. The present work focuses on quality assurance with compensation using nature inspired algorithms with DG placement after tracking. It compensates by adding fact devices. MBO, GWO and Cuckoo search algorithms are used in the present research and devices like DVR and Dstat.Com are added along with DG. The quality parameters are compared with the existing and in order to increase the robustness hybrid algorithms with any two are suggested.
Various real-time applications can be handled through wireless sensor networks, which consist of a wide range of sensor nodes. A novel congestion control mechanism is proposed on optimized rates for energy-efficient transmissions. To reduce energy consumption across the network, a rate-based congestion control algorithm based on cluster routing is presented. By reducing the end-to-end delay, rate control improves the network life time over a large simulation period. Clustering is initially performed using novel routing algorithms. After that, rate control is implemented using an energy optimization strategy suitable for high packet delivery ratios. Finally, packets are sent with maximum throughput using regionOptimization-driven routing. The simulation is performed on the NS2 simulation platform. Finally, performances are evaluated with respect to average delay in end to end nodes, delivery ratio ofpackets, throughput, energy efficiency, energy consumption and reliability. Novel routing with an energy optimal algorithm (NREOA) reduces energy consumption as the network progresses and a variation of 20% compared with existing protocols.
Crime against women, a never-ending issue is a sad reality that demands focused attention. The occurrence of crimes in different states in India varies a lot. Multi-Criteria Decision Making (MCDM) method, called TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is applied on real occurrences of crime to develop women vulnerability index (WVI). This index measures the susceptibility of women to crime in any region of India. This marks the first instance of applying MCDM technique (TOPSIS) to derive such an index for crime against women. The index will equip the law enforcing agencies and various NGOs to assess the susceptibility of Indian women in different regions and take appropriate action for mitigation of such crimes to create a safe environment for women. We find that states like Mizoram, Nagaland, Sikkim in northeast India and Lakshadweep Islands in southern India have very low values of the index and are the safest places for women. On the other hand, Uttar Pradesh, Delhi, Haryana, Rajasthan, and Bihar are Indian states where women are most susceptible to crime having very high values of WVI.
Crime against women is a chronic issue that saddens society and needs to be carefully addressed. The states of India exhibit significant variations in the incidence of criminal behavior. We created a novel index in our previous research to determine how vulnerable Indian women are to crimes in various Indian states and union territories. The Women Vulnerability Index (WVI) assessed women's vulnerability to crime across all regions of the country. A set of alternatives is ranked using Multi-Criteria Decision Making (MCDM) procedures based on a range of criteria or objectives. There are different MCDM techniques available to choose from. Also, many weighting schemes exist to assign relative importance or weight to the indicators. The task of selecting the MCDM technique for one's application is a big challenge. A bigger challenge is to select the appropriate weighting mechanism as well. This study's primary goal is to evaluate the viability and efficacy of several MCDM approaches in conjunction with various weighting systems in order to identify the Indian state with the greatest rate of crime against women. We apply different MCDM techniques on crime data to compute WVI. Also, we see the effect of using six different methods to assign weights to the factors on the values of WVI. MCDM methods are very popular these days and are being used in a lot of domains for decision making applications. Our paper will guide all such stakeholders and researchers to choose an appropriate MCDM technique and weighting method for their applications.
Cultivating rice is crucial in India to meet demands of a growing population. In order to improve crop yield, it's essential to address factors like diseases caused by bacteria, fungi, and viruses. Detecting and managing these diseases is vital, and one effective approach is employing rice plant disease detection methods. Deep learning techniques, known for their ability to analyse data, are used for disease identification in plants. This work explores various deep learning approaches for detecting rice plant disease. Deep learning, particularly in computer vision, has shown significant progress in detecting plant diseases. The study compares the effectiveness deep learning mechanisms, demonstrating superior performance of deep learning models. Utilizing deep learning can help prevent major crop losses by detecting leaf diseases through image analysis.
The embedded steel is integrated with concrete material, primarily used in buildings and infrastructure projects. "Embedded steel" refers to steel reinforcement bars or mesh embedded in concrete structures. Steel is added to the concrete to strengthen and support the structure. One of the primary challenges associated with embedded-based steel is anticipating its corrosion once it has been incorporated into building structures. It is necessary to monitor the initiation time of corrosion on the steel in the concrete, which is considered crucial to the environment. Early corrosion detection is challenging, and its accuracy helps design durable concrete. This process reduces the time and cost of embedded steel manufacturing. This research focuses on applying embedded deep-learning models to test the accuracy of the algorithms suggested for embedded steel. A-state of art technique reveals that convolutional neural network (CNN), Long short-term memory (LSTM), and Deep neural network (DNN) models can perform accurate predictions. In this study, the above deep learning models are embedded to validate the accuracy of the different algorithms. The study aimed to determine the corrosion initiation time on steel, which is Incorporated within concrete via corrosion potential measurement. To achieve this, concrete samples were arranged with conch shell powder as a partial replacement to Portland cement and exposed in 5% sodium chloride with following the requirements of ASTM C876 – 15. During the exposure time, the steel embedded's corrosion potential was measured, and the resulting dataset was utilized for training three deep-learning models. These models were developed using input variables such as cement, conch shell powder, fine aggregate, coarse aggregate, exposure period, and water to estimate the corrosion initiation time on the embedded- steel based on the potential corrosion measurements.
One of the most common natural disasters is flooding that endangers infrastructure and life of human beings, particularly in heavily populated areas. The ability to identify flooded regions quickly and precisely is critical for emergency response planning and damage assessment. This research is aimed at mapping the flooded regions as per their severity levels to improve community resilience and decision making in disaster scenarios. To accomplish this task, image classification technique is used. In this study, for the purpose of classification our designed dataset having images of the flood of varying severity levels are categorized into three classes viz mild, moderate, and severe. Further to improve the classification task, Convolutional Neural Networks (CNNs) with transfer learning approach is used. CNN is powerful enough to extract features from large volumes of visual data and is particularly excellent at exploiting semantic information, however, requires huge amount of training data. In this article instead of building and training a CNN from start for flood severity image classification, pre-built and pre-trained networks via transfer learning are used. A comparative analysis using VGG16, MobilNet, and ResNet50 (which are prominent CNN pretrained models) has been performed in this study. The average recall, precision, and F1-score are used to assess performance. Experiment analysis shows that fine-tuned pretrained ResNet50 model performs better as compared to state of art models for flood image classification application. DOI: https://doi.org/10.17762/ijisae.v10i3S.2426
Answering a question from a given visual image is a very well-known vision language task where the machine is given a pair of an image and a related question and the task is to generate the natural language answer. Humans can easily relate image content with a given question and reason about how to generate an answer. But automation of this task is challenging as it involves many computer vision and NLP tasks. Most of the literature focus on a novel attention mechanism for joining image and question features ignoring the importance of improving the question feature extraction module. Transformers have changed the way spatial and temporal data is processed. This paper exploits the power of Bidirectional Encoder Representation from Transformer (BERT) as a powerful question feature extractor for the VQA model. A novel method of extracting question features by combining output features from four consecutive encoders of BERT has been proposed. This is from the fact that each encoder layer of the transformer attends to features from the word to a phrase and ultimately to a sentence-level representation. A novel BERT-based hierarchical alternating co-attention VQA using the Bottom-up features model has been proposed. Our model is evaluated on the publicly available benchmark dataset VQA v2.0 and experimental results prove that the model improves upon two baseline models by 9.37% and 0.74% respectively. DOI: https://doi.org/10.17762/ijisae.v10i3S.2427
As per World Health Organization (WHO), avoiding touching the face when people are in public or crowded places is an effective way to prevent respiratory viral infections.This recommendation has become more crucial with the current health crisis and the worldwide spread of COVID-19 pandemic.However, most face touches are done unconsciously, that is why it is difficult for people to monitor their hand moves and try to avoid touching the face all the time.Hand-worn wearable devices like smartwatches are equipped with multiple sensors that can be utilized to track hand moves automatically.This work proposes a smartwatch application that uses small, efficient, and end-to-end Convolutional Neural Networks (CNN) models to classify hand motion and identify Face-Touch moves.To train the models, a large dataset is collected for both left and right hands with over 28k training samples that represents multiple hand motion types, body positions, and hand orientations.The app provides real-time feedback and alerts the user with vibration and sound whenever attempting to touch the face.Achieved results show state of the art face-touch accuracy with average recall, precision, and F1-Score of 96.75%, 95.1%, 95.85% respectively, with low False Positives Rate (FPR) as 0.04%.By using efficient configurations and small models, the app achieves high efficiency and can run for long hours without significant impact on battery which makes it applicable on most off-the-shelf smartwatches.
Due to the rapid spread of corona virus disease , it has been considered as a pandemic throughout the world.The misclassification of COVID-19 cases may even lead the death of the patients, and hence the diagnosis at early stage is important to stop further spread of the infection and to safeguard the life of the patients.This paper proposes the Aquila tuned Deep neural network (Aquila-DNN) classifier for the classification of COVID-19 patients using the chest image data assessed through Wireless sensor Network (WSN).The extraction of important features from the chest image data is important in the diagnosis as it encloses the important data of the patients.The optimal tuning of the DNN parameters using the Aquila Optimizer (AO) assists in improving the classification accuracy of proposed model.In addition, the convergence is also boosted using the tuning process of the AO algorithm.The effectiveness of the proposed Aquila-DNN model is validated with the analysis of the model based on the performance indices, namely accuracy, ROC curve, and F1 measure.The testing accuracy and the training accuracy of Aquila-DNN model are attained to be 99.7%, and 95.4545%, respectively.
The assignment problem is one of the most popular optimization problems where the main objective is to find the total minimal cost for assigning n objects to n other objects.This paper presents an original heuristic, named Dhouib-Matrix-AP1, to generate an initial basic feasible solution for the classical assignment problem in very easy and fast steps.The proposed method Dhouib-Matrix-AP1 finds the optimal or a near optimal solution for the assignment problem after just n iterations with only three easy steps in each one.The first step consists in computing the total cost by rows and columns using an original formula (Sum -(Min * n /2)).The second step looks for selecting the greatest value (Z) from these total costs.Finally, the third step is based on choosing the minimal row or column matrix element which corresponds to the value Z.For this purpose, we generate a detailed step by step process application based on 4x4 dimensional sample.Moreover, a stepwise application of the Dhouib-Matrix-AP1 method is presented with details for three examples.Besides, the results of a set of fifteen literature examples with different dimensions are discussed.The outcomes of the study show that the proposed method provides the optimal or near optimal solutions in easier and faster manner.
Drowsiness is one of the major reasons that causes traffic accidents.Thus, its early detection can help preventing accidents by warning the drivers before the unfortunate events.This study focuses on the detection of drowsiness using classification of alpha waves from EEG signals with 25 different machine learning algorithms.The results were evaluated in terms of classification accuracy and classification time.Accordingly, the Bagged Trees and Subspace k-Nearest Neighbor models gave better results in terms of classification accuracy compared to the Tree algorithm methodology, although the classification times are relatively high.Tree Algorithms approach displays optimal features as it serves as both a considerably satisfactory classification accuracy in much shorter times.The requirements in terms of accuracy and time for the recognition of drowsiness should determine the method to be applied.
A methodology for project management refers to a set of guidelines that defines how to work and communicate while working as a project member.Waterfall practice is the old methodology.As a response to dealing with the difficulty of software development, it has turned out to the most widely utilized methodologies of project management in the software and management industries.Oher software development focused project management method, Agile, has appeared as a response to the shortcoming of Waterfall tool for handling complex projects.Lean Six Sigma is the combination of the main strategies of Six Sigma and Lean.This paper aims to reveal success factors of these three project management methodologies employing Fuzzy cognitive map (FCM) technique, which combines fuzzy logic and neural networks.Presence of cause-and-effect relationships between pair of success indicators and unavailability of crisp data led us to use FCM method in order to determine the most significant criteria of these project management methodologies.This is the first study that considers multiple and conflicting criteria of success factors of waterfall, agile, and lean six sigma project management methodologies.There is no study that aims to provide success criteria evaluation of waterfall, agile, and lean six sigma project management methodologies.This assessment is crucial for companies that have to be managed effectively their project processes in increasing technology and market competition.FCM is a suitable tool to solve this problem since it considers positive and negative relationships, causal links among criteria with their direction, and it is applicable in the absence of crisp data.
