Photovoltaic Charging Station Site Selection using a Multi-Criteria Decision Making (MCDM) Framework with a Novel Criterion Identification

Abedallah Z.; Rasha

doi:https://doi.org/10.54216/IJWAC.040203

Journal of Cosmology, Filaments and Astrobiology | 2022

Our universe is but one page in a large book, each with different sets of physical laws and types of Consciousness

Our universe is but one page in a large book. For example, things and Beings can travel between Universes, intentionally or unintentionally. The pages of the "book" of Universe are connected at a common point and move outwards in a rotation, overlaid in a spiral manner, related to the phi ratio. Each "page" which is "touching" the next page and the previous page, has physical laws and forms of Consciousness that are only slightly different from one another.

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Robert N. Boyd mail

link https://doi.org/10.54216/JCFA.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Journal of Cosmology, Filaments and Astrobiology | 2022

"... we lit beacons... in the Universe only we are alone ..."

Superluminal contact with extraterrestrial civilizations can be carried out either by observing the appearance   of  regular gaps in the CMB relic microwave background or by manipulating the state of  quantum   fluctuation using the dynamic Casimir effect; potentially, it is also possible to observe patterns in the relic background of Goldstone bosons – axions. It is more correct to evaluate the search for a  signal on an axion background of noise as a spectrum of intermediate NeutralA - gaps in various relict  backgrounds that form a recognizable pattern of NonA formed NeutralA and AntiA, which is the neutrosophic signal of NonA for us. Observation of the NonA neutrosophic signal for axions is possible               with scalar/vector potential detectors based on the Aharonov-Bohm effect or on the basis of the Wolf-Bragg`s condition for X-ray diffraction/interference. The creation of axion telescopes with matrices of  nano- or micron-sized pixels for observing the cosmic NonA for Rφ and RA will make it possible to establish superluminal Contact of Civilizations.  

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Alexander B. Ilin mail

link https://doi.org/10.54216/JCFA.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Fusion: Practice and Applications | 2022

Machine Learning Data Fusion for Plant Disease Detection and Classification

  It is crucial to quickly identify plant diseases since they impede the development of affected plants. Despite the widespread use of Machine Learning (ML) models for this purpose, the recent advances in a subset of ML known as Deep Learning (DL) suggest that this field of study has much room for improvement in terms of detection and classification accuracy. To identify and categorize plant diseases, a wide variety of established and customized DL architectures are deployed with several visual analysis methods. In this study, we use deep learning techniques to create a model for a convolutional neural network that can identify and diagnose plant diseases using very basic photos of healthy and sick plant leaves. The models were trained using an open library of 20639 photos that included both healthy and diseased plants across 15 different classifications. Some model architectures were trained, with the highest performance obtaining a success rate of 97.70% in detecting the correct [plant, illness] pair (or healthy plant). Due to its impressive success rate, the model is a valuable advising or early warning tool, and its technique might be developed to help an integrated plant disease diagnosis system function in actual production settings.

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El Mehdi Cherrat mail -

Amine Saddik mail

link https://doi.org/10.54216/FPA.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Fusion: Practice and Applications | 2022

Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion

Among women, breast cancer has a high incidence and high fatality rate. Due to a lack of early detection facilities and barriers to accessing technological improvements in battling this illness, mortality rates are disproportionately greater in underdeveloped countries. Biopsies done by trained pathologists are the only certain approach to diagnosing cancer. With the use of computer-aided diagnostic algorithms, pathologists may improve their efficiency, objectivity, and consistency in making diagnoses. A key goal of this research is to create an accurate automated system for diagnosing breast cancer that can function in the current clinical setting. In this work, we offer an algorithm for the identification of breast cancer that uses asymmetric analysis as the basic choice and decision-level fusion. Fusion of local nuclei features extracted using convolutional neural network (CNN) models pre-trained on the database constitutes the picture feature representation. The dataset is accessible for public use, and the results are evaluated by running 25 random trials with an 80%-20% split between train and test. Overall, the suggested framework was 86%. The proposed framework is shown to outperform numerous current methods and to provide results on par with the state-of-the-art techniques without requiring extensive computing resources. Breast cancer detection from histological pictures may be greatly aided by the use of this qualitative approach based on transfer learning.

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Surinder Kaur mail -

Javalkar Dinesh Kumar mail -

Gopal Chaudhary mail -

Manju Khari mail

link https://doi.org/10.54216/FPA.080105

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Fusion: Practice and Applications | 2022

Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion

Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.

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Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/FPA.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2022

A Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform

Largescale IoT applications with thousands of geo distributed IoT gadgets making huge volumes of data impose immense challenges to designing transmission mechanisms that offer data transfer has less latency and great scalability. In this work, an investigation of a hierarchical Edge-Cloud publishes or subscribe brokers method was performed with the help of an effective two-tier routing structure for alleviating such problems whenever sending event notices in large scale IoT mechanisms. In this technique, IoT gadgets use the benefits of nearby edge brokers deliberately positioned in edge network for data supplying services for minimizing latency. This manuscript introduces a Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform, named MOCRS-IoTMEC model. The projected MOCRS-IoTMEC model is mainly concentrated on the identification of optimal routes in the IoT assisted MEC environment by the use of pigeon inspired optimization (PIO) algorithm. Also, the LEACH protocol is applied to initially cluster the IoT devices. The PIO algorithm is applied to determine the fitness function to choose optimal routes. To depict the enhanced performance of the MOCRS-IoTMEC model, a detailed comparison study is made. The experimental outcomes reported the enhanced execution of the MOCRS-IoTMEC method over other approaches.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/IJWAC.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2022

Clustered IoT Based Data Fusion model for Smart Healthcare Systems

Futuristic sustainable computing solutions in e-healthcare applications were depends on the Internet of Things (IoT) and cloud computing (CC), has provided several features and realistic services. IoT-related medical devices gather the necessary data like recurrent transmissions in health limitations and upgrade the exactness of health limitations all inside a standard period. These data can be generated from different types of sensors in different formats. As a result, the data fusion is a big challenge to handle these IoT-based data. Moreover, IoT gadgets and medical parameters based on sensor readings are deployed for detecting diseases at the correct time beforehand attaining the rigorous state. Machine learning (ML) methods play a very significant task in determining decisions and managing a large volume of data. This manuscript offers a new Hyperparameter Tuned Deep learning Enabled Clustered IoT Based Smart Healthcare System (HPTDLEC-SHS) model. The presented HPTDLEC-SHS technique mainly focuses on the clustering of IoT devices using weighted clustering scheme and enables disease diagnosis process. At the beginning level, the HPTDLEC-SHS technique exploits min-max data normalization technique to convert the input data into compatible format. Besides, the gated recurrent unit (GRU) model is utilized to carry out the classification process. Finally, Jaya optimization algorithm (JOA) is exploited to fine tune the hyperparameters related to the GRU model. To demonstrate the enhanced performance of the HPTDLEC-SHS technique, an extensive comparative outcome highlighted its supremacy over other models.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/JISIoT.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Fusion: Practice and Applications | 2022

Early Detection of Cardiovascular Diseases using Deep Learning Feature Fusion and MRI Image Analysis

Deaths from cardiovascular disease (CVD) are more common than any other kind of mortality in the world. Electrocardiograms, two-dimensional echocardiograms, and stress tests are only a few of the diagnostic tools available to combat the rising incidence of cardiovascular disease. Since the electrocardiogram (ECG) is a clinical therapeutic agent that does not need any intrusive procedures, it may be used to diagnose cardiovascular disease (CVD) early and prescribe the appropriate treatment to prevent its fatal consequences. However, it may be time-consuming and demanding for a physical examination to interpret all these signals from various pieces of equipment, especially if they are non-stationary and repeating. It is necessary to use a computer-assisted model for rapid and precise prediction of CVDs since the Heart Signal from an ECG machine is not a stationary sign, the differences may not be repeated and may manifest at different intervals. In this paper, we offer a fully deep convolutional neural network-based automated diagnosis technique for cardiovascular illness. In order to extract those form characteristics from the Kaggle cardio-vascular disease dataset, CVD-MRI is employed in this detection method. In this case, the risk of cardiovascular disease is estimated using a completely deep convolution neural network and deep learning convolution filters (CVD). The suggested operation's main goal is to "improve the accuracy of completely deep convolution neural network while simultaneously reducing the complexity of the computation and the cost function." Accuracy of 88 percent is achieved by the proposed fully deep convolutional neural network.

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

Saleh A. Almutairi mail

link https://doi.org/10.54216/FPA.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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American Journal of Business and Operations Research | 2022

An Innovative Multi-Criteria Decision-Making (MCDM) Framework for Picking the Right Used Chemical Tankers: A Classified Model-Based Discussion

Because chemical tanker boats are so expensive to build and maintain, shipping firms may not be able to supply their clients with fair transportation pricing. As a result, shipping businesses may find various benefits and chances by purchasing second-hand chemical tanker vessels. But picking a chemical tanker is a hard task that requires overcoming numerous misunderstandings and weighing several conflicting factors.  A novel MCDM technique has been proposed in this study for this aim. EDAS approach is used in the proposed model, to handle uncertainty. In order to demonstrate efficacy, relevance, and robustness, the model was used to address decision-making issues involving the selection of suitable second-hand chemical tankers from a pool of 10 (alternatives). The chemical tanker boats were evaluated using 14 distinct choice criteria in the present article. The findings show that the most important factor is "CTC6′′ Maintenance cost," and the best and most preferred chemical tanker is "CTA6"

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail -

William Thompson mail

link https://doi.org/10.54216/AJBOR.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2022

Photovoltaic Charging Station Site Selection using a Multi-Criteria Decision Making (MCDM) Framework with a Novel Criterion Identification

Charging points on islands are becoming highly essential due to growing environmental concerns and an increase in the number of electric ships that need to be recharged. Site choice is the first step, but there is not enough research on island photovoltaic charging station site selection (IPVCS). To select the best IPVCS site, a multi-criteria decision-making framework (MCDM) is proposed. As a result of this structure, a new set of criteria for evaluating ships is formed, and current criteria are used to suggest two new ones: "Likelihood of adverse weather" and "Charging distance of the ship." Simultaneously time, the correlation among criteria is shaky at best. Therefore, the weight of the criteria is determined first. Then the rank of the alternatives is computed by the simultaneous evaluation of criteria and alternatives (SECA). Multi-criteria techniques like SECA may be used to objectively and accurately determine the weights of criteria. The best alternative is PVC3 followed by PVC1 then PVC2 then PVC4.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/IJWAC.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new