Design Principles of Sustainable Website Powered by Solar Energy

The present paper addresses the design of a sustainable green hosting website powered by solar energy as a type of renewable energy. Two websites are designed: a static website with a carbon dioxide-free address and a dynamic website with an organic address. The server used in the design process uses solar energy as a source of electricity

Using Social Media Campaigns to Activate Electronic Volunteering Platforms during COVID-19 Pandemic

We design and implement an advertising campaign using social media to encourage people to volunteer on online platforms as well as identify the effectiveness of online marketing in informing people and encouraging them to participate in volunteer platforms. It is shown that the expected turnout of young people to volunteer work through social networking platforms and awareness of the epidemic reduced its prevalence rate

Using Social Media Campaigns to Activate Electronic Volunteering Platforms during COVID-19 Pandemic

We design and implement an advertising campaign using social media to encourage people to volunteer on online platforms as well as identify the effectiveness of online marketing in informing people and encouraging them to participate in volunteer platforms. It is shown that the expected turnout of young people to volunteer work through social networking platforms and awareness of the epidemic reduced its prevalence rate

Analysis of Attitudes towards Food Waste in the Kingdom of Saudi Arabia Using Fuzzy Logic

Attitudes and feelings towards food waste and positions on management policies have been vastly increased over the past few decades. Most of the available research on the analysis of attitudes towards food waste have been carried out using conventional statistical methods. This paper aims to assess and analyse attitudes and preferences of young Saudi females towards a number of policies and plans that are designed to meeting sustainable targets, using fuzzy logic analysis. This is a very important aim, especially since Vision 2030 in Saudi Arabia puts a major emphasis on sustainability, setting many resources to tackle environmental problems and achieving better social standards. The Methodology includes designing and collecting data from 199 participants using a questionnaire that includes 23 questions. Data were obtained from students at Princess Nourah bint Abdulrahman University (PNU). The analysis includes utilising artificial intelligence (AI) techniques. Fuzzy logic analysis has been widely used in many fields, but has not seen many applications on food waste analysis and attitudes. Fuzzy logic analysis has the advantage of producing efficient results from smaller sample sizes and, in particular, with qualitative characteristics of the used indicators. The participants expressed positive preferences and attitudes towards the programs and policies that are designed to achieve sustainability and manage food waste. The results show that over 25% of them prefer the option of “storage for reuse” of food waste, over 35% prefer the option of distribute it to needed families and over 30% opted to the option of recycling to fertilisers. The study also reveals a very good level of awareness and appreciation of food waste and plans associated with it. The implications from this study suggest that despite the positive attitudes, there still is more research needed to obtain full understanding of attitudes towards food waste from the whole range of the population in order to gain knowledge and build specific programs to reduce food waste and achieve sustainability in the country

Using Artificial Intelligence for Optimizing Natural Frequency of Recycled Concrete for Mechanical Machine Foundation

This paper investigates the mechanical properties of two different types of recycled concrete, which use wood and rubber, relative to those characteristics of pure concrete, in terms of maximum load and natural frequencies. This paper contributes to the state of the art in this area in a number of ways. Firstly, the paper provides furtherance to the progressively growing literature in the field of recycled concrete and mechanical properties of materials. Secondly, the paper investigates the mechanical properties of two different types of recycled concrete by means of investigating the natural frequency of the samples, which is a new contribution. Lastly, the results from predicting the natural frequencies of concrete using fuzzy logic have been effectively assessed and compared with the analytical results. Results from the study show that the pure concrete samples produced maximum natural frequency, then concrete samples with wood, and lastly, concrete samples with rubber. The tolerance between the lab test results and fuzzy logic is approximately 5%. These results could have significant implications for furthering recycled concrete research and for designing machine foundations. Evidence of the applicability of fuzzy logic as a predictive and analysis tool for the mechanical properties of recycled concrete are discussed

An Investigation into Conversion of a Fleet of Plug-in-Electric Golf Carts into Solar Powered Vehicles Using Fuzzy Logic Control

This paper presents an investigation factors that need to be considered in the design and selection of components for the conversion of a fleet of plug-in electric golf carts at Princess Nourah Bint Abdelrahman University, (PNU), Riyadh, Kingdom of Saudi Arabia (KSA), into solar power energy. Currently, the plug-in electric golf carts are powered by a set of deep-cycle lead-acid battery packs consisting of six units. Solar energy systems (photovoltaics and solar thermal) provide significant environmental benefits and opportunities over the traditional and conventional sources. Therefore, they can contribute positively to many aspects of the built environment and societies. There are many factors that affect the energy generated from the solar panel system. These include type and dimension of the solar panels, weight, speed, acceleration, and other characteristics of the used golf carts, and the energy efficiency of the solar energy system, as main factors that affect the green energy generated to operate the carts. The energy values needed to power the electric cart were calculated and optimized using traction energy calculation and optimized using a fuzzy logic analysis. The fuzzy logic system was developed to assess the impacts of varying dimensions of solar panel, vehicle speed, and weight on the energy generation. Initial calculations show that the replacement cost of the batteries can be up to approximately 75 percent of the operating cost. Together with the indirect cost benefits of achieving zero tail-pipe emission and the comfort of silent operation, the cost of operation using solar energy can be significant when compared with the cost of battery replacement. In order to achieve better efficiency, supercapacitors can be investigated to replace the conventional batteries. The use of fuzzy logic successfully facilitated the optimization of system operation conditions for best performance. In this study, fuzzy logic and calculated data were used as an optimization tool. Future work may be able to use fuzzy logic with experimental data to demonstrate feasibility of utilizing fuzzy logic systems to assess energy generation processes. Future investigations could also include investigation of other factors and methodologies, such as various types of batteries, supercapacitors, solar panels, and types of golf carts, together with different techniques of artificial intelligence to assess the optimum system specifications

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis

Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Preservation of Sensitive Data Using Multi-Level Blockchain-based Secured Framework for Edge Network Devices

The proliferation of IoT devices has influenced end users in several aspects. Yottabytes (YB) of information are being produced in the IoT environs because of the ever-increasing utilization capacity of the Internet. Since sensitive information, as well as privacy problems, always seem to be an unsolved problem, even with best-in-class in-formation governance standards, it is difficult to bolster defensive security capabilities. Secure data sharing across disparate systems is made possible by blockchain technology, which operates on a decentralized computing paradigm. In the ever-changing IoT environments, blockchain technology provides irreversibility (immutability) usage across a wide range of services and use cases. Therefore, blockchain technology can be leveraged to securely hold private information, even in the dynamicity context of the IoT. However, as the rate of change in IoT networks accelerates, every potential weak point in the system is exposed, making it more challenging to keep sensitive data se-cure. In this study, we adopted a Multi-level Blockchain-based Secured Framework (M-BSF) to provide multi-level protection for sensitive data in the face of threats to IoT-based networking systems. The envisioned M-BSF framework incorporates edge-level, fog-level, and cloud-level security. At edge- and fog-level security, baby kyber and scaling kyber cryptosystems are applied to ensure data preservation. Kyber is a cryptosystem scheme that adopts public-key encryption and private-key decryption processes. Each block of the blockchain uses the cloud-based Argon-2di hashing method for cloud-level data storage, providing the highest level of confidentiality. Argon-2di is a stable hashing algorithm that uses a hybrid approach to access the memory that relied on dependent and independent memory features. Based on the attack-resistant rate (> 96%), computational cost (in time), and other main metrics, the proposed M-BSF security architecture appears to be an acceptable alternative to the current methodologies

Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model’s training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods