Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

The growth of commercial activity and the transportation of goods around the world has increased the challenges of stevedoring within ports. In case of loading and unloading ships safely, quickly, and efficiently, goods movable harbor cranes play an important role. This work aims to propose an industrial internet of things (IIoT)-based quality control system for logistic services ports goods movable harbor crane (QC-GMHC). The GMHC system based on using programmable logic controller (PLC), along with a multi-sensor data collecting system. Several operations have been done to establish the QC-GMHC system as: GMHC sensors real-time data storage, and data sharing; monitoring the GMHC status (remote-local); and the efficiency reporting. In order to validate the proposed system’s hardware, it was used in an already operational GMHC for six months, during which data were collected and analyzed. The results revealed that the proposed hardware system worked efficiently for 24 hours. To forecast the efficiency of the GMHC, a deep learning (DL) conventional long short-term memory (LSTM) and neural network model was trained and validated using synthetic data generated from acquired real data. The results showed that QC-GMHC can calculate efficiency with an accuracy of 80%, which is sufficient for our application.

The measurement of distance between research disciplines involves various approaches, with a focus on publication citation analysis. However, calculating discipline distance requires more than just selecting relevant information; it also involves choosing suitable quantification methods and similarity measures. In this paper, we introduce a novel approach to measuring the distance between research disciplines, referred to as a distance matrix. This approach is particularly useful when there is limited availability of citation data, providing an alternative method for quantifying the distance between disciplines. Our method counts co-occurrences of disciplines based on researcher collaborations in projects and evaluates various similarity measures to convert the co-occurrence matrix into a similarity matrix. We analyze the behavior of different similarity measures and propose functions to transform the similarity matrix into a distance matrix, capturing research discipline dissimilarity effectively. Additionally, we establish evaluation criteria for distance matrix quality. We implement our approach on the Flanders Research Information Space dataset, showing promising results. The distance matrix demonstrates satisfactory density scores, outperforming traditional approaches in skewness and deviation. The probability density functions of distances remain consistent over time, indicating stability. Furthermore, the distance matrix proves valuable for visualizing discipline profiles associated with the dataset, providing valuable insights.

Numerous medical studies have shown that Alzheimer's disease (AD) was present decades before the clinical diagnosis of dementia. As a result of the development of these studies with the discovery of many ideal biomarkers of symptoms of Alzheimer's disease, it became clear that early diagnosis requires a high-performance computational tool to handle such large amounts of data, as early diagnosis of Alzheimer's disease provides us with a healthy opportunity to benefit from treatment. The main objective of this paper is to establish a complete framework that is based on deep learning approaches and convolutional neural networks (CNN). Four stages of AD, such as (I) preprocessing and data preparation, (II) data augmentation, (III) cross-validation, and (IV) classification and feature extraction based on deep learning for medical image classification, are implemented. In these stages, two methods are implemented. The first method uses a simple CNN architecture. In the second method, the VGG16 model is the pre-trained model that is trained on the ImageNet dataset but applies the same model to the different datasets. We apply transfer learning, meaning, and fine-tuning to take advantage of the pre-trained models. Seven performance metrics are used to evaluate and compare the two methods. Compared to the most recent effort, the proposed method is proficient of analyzing AD, moreover, entails less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments. The experimental findings demonstrate that the suggested designs are appropriate for basic structures with minimal computational complexity, overfitting, memory consumption, and temporal regulation. Besides, they achieve a promising accuracy, 99.95% and 99.99% for the proposed CNN model in the classification of the AD stage. The VGG16 pre-trained model is fine-tuned and achieved an accuracy of 97.44% for AD stage classifications.

Identifying interdisciplinary research has become an important area of study in scientometrics. However, defining what exactly constitutes interdisciplinarity and how it manifests in research activities, such as publications or research projects, remains challenging. In this paper, we propose a mathematical modeling approach to interdisciplinarity measurement based on assessing project diversity. Particularly, we propose a novel approach that combines three indicators: the diversity of researchers, the diversity of research organizations, and the diversity of research disciplines involved in the project, to identify potentially interdisciplinary research projects. To measure diversity, we employ various methods, including distance matrix calculation, evaluation of the distance between researchers, and assessment of the relevancy of researchers' expertise to the projects. We implemented the proposed approach on two datasets; FRIS and Dimensions. We could classify the interdisciplinarity of projects into three groups-Low, Medium, and High. Empirical results analysis supports the proposed approach assumption that the diversity of research projects gets higher when the distances between disciplines in the projects increase. Further, it was shown that the diversity of researchers and organizations was strongly affected by the distance. The number of researchers and organizations had a relatively small impact on the overall diversity score. Furthermore, the relevancy weight can be incorporated as an additional factor in the measurement of interdisciplinary.

The Internet of Robotic Things (IoRT) is an integration between autonomous robots and the Internet of Things (IoT) based on smart connectivity. It's critical to have intelligent connectivity and excellent communication for IoRT integration with digital platforms in order to maintain real-time engagement based on efficient consumer power in new-generation IoRT apps. The proposed model will be utilized to determine the optimal way of task offloading for IoRT devices for reducing the amount of energy consumed in IoRT environment and achieving the task deadline constraints. The approach is implemented based on fog computing to reduce the communication overhead between edge devices and the cloud. To validate the efficacy of the proposed schema, an extensive statistical simulation was conducted and compared to other related works. The proposed schema is evaluated against the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), and Salp Swarm Algorithm to confirm its effectiveness. After 200 iterations, our proposed schema was found to be the most effective in reducing energy, achieving a reduction of 22.85%. This was followed closely by GA and ABC, which achieved reductions of 21.5%. ALO, WOA, PSO, and GWO were found to be less effective, achieving energy reductions of 19.94%, 17.21%, 16.35%, and 11.71%, respectively. The current analytical results prove the effectiveness of the suggested energy consumption optimization strategy. The experimental findings demonstrate that the suggested schema reduces the energy consumption of task requests more effectively than the current technological advances.

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus prediction with deep learning (DMPDL) framework structure. The DMPDL framework proposes the Gaussian Modification of Grey Wolf Optimization Algorithm (GMGWO), which is used to identify the ideal subset of characteristics and reduce classification error. It is crucial to offer an accurate optimizer that can be used to predict diabetic disorders more quickly and accurately if this framework is provided to address such concerns. The GMGWO was created to identify new areas to look for. Following that, it is examined using the CEC2017 benchmark features. The experimental findings show that the DMPDL framework outperforms the others in all circumstances, with an accuracy of 82.72% for the Pima Indian Diabetes (PIDM) dataset, and 97.63% for the Early-Stage Diabetes Risk (ESDR) dataset

Open finance is evolving and extending open banking. This creates a large context that implies a financial and identity data exchange paradigm, which faces challenges to balance customer experience, security, and the self-control over personal identity information. We propose Self-Sovereign Banking Identity (SSBI), a Blockchain-based self-sovereign identity (SSI) to secure private data sharing by utilizing trusted customer's banking cards as a key storage and identity transaction-signing enclave. The design and implementation of the SSI framework is based on the Veramo SDK and Ethereum to overcome the limitation of signing curve availability on the current banking Java Cards needed for Hyperledger Indy. SSBI uses the elliptic curve SECP256K1 for transaction signing, which exists for several payment cards in the market. SSBI enables automated financial services and trust in the service provider communication. This work analyzes the flow and framework components, and evaluates the usability, integration, and performance in terms of throughput, latency, security, and complexity. Furthermore, the proposed approach is compared with related solutions. The presented prototype implementation is based on a test Ethereum network and signing transactions on the banking card. The preliminary results show that SSBI provides an effective solution for integrating the customer's banking cards to secure open banking identity exchange. Furthermore, it allows the integration of several scenarios to support trusted open banking. The Blockchain layer settings need to be scaled and improved before real-world implementation.

Forecasting research disciplines associated with research projects is a significant challenge in research information systems. It can reduce the administrative effort involved in entering research project-related metadata, eliminate human errors, and enhance the quality of research project metadata. It also enables the calculation of the degree of interdisciplinarity of these projects. However, predicting scientific research disciplines and measuring interdisciplinarity in a research endeavor remain difficult. In this paper, we propose a framework for predicting the research disciplines associated with a research project and measuring the degree of interdisciplinarity based on associated metadata to address these issues. The proposed framework consists of several components to improve the performance of research disciplines prediction and interdisciplinarity measurement systems. These include a feature extraction component that utilizes a topic model to extract the most appropriate features. Further, the framework proposes a discipline encoding component that applies a data mapping strategy to lower the dimensionality of the output variables. Furthermore, a distance matrix creation component is proposed to recommend the most appropriate research disciplines and compute interdisciplinarity associated with research projects. We implemented the suggested framework on two separate research information systems databases for research projects, Dimensions and the Flemish Research Information Space. Experimental results demonstrate that the proposed framework predicts the research disciplines associated with research projects more accurately than related work.

Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress of Alzheimer’s patients and the lack of accurate diagnostic tools, early detection and classification of Alzheimer’s disease are open research areas. Accurate detection of Alzheimer’s disease in an effective way is one of the many researchers’ goals to limit or overcome the disease progression. The main objective of the current survey is to introduce a comprehensive evaluation and analysis of the most recent studies for AD early detection and classification under the state-of-the-art deep learning approach. The article provides a simplified explanation of the system stages such as imaging, preprocessing, learning, and classification. It addresses broad categories of structural, functional, and molecular imaging in AD. The included modalities are magnetic resonance imaging (MRI; both structural and functional) and positron emission tomography (PET; for assessment of both cerebral metabolism and amyloid). It reviews the process of pre-processing techniques to enhance the quality. Additionally, the most common deep learning techniques used in the classification process will be discussed. Although deep learning with preprocessing images has achieved high performance as compared to other techniques, there are some challenges. Moreover, it will also review some challenges in the classification and preprocessing image process over some articles what they introduce, and techniques used, and how they solved these problems.

Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the entire network will fail. This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention (HDLIDP) framework, which blends signature-based and deep learning neural networks to detect and prevent intrusions. This framework improves detection accuracy while addressing all of the aforementioned problems. To validate the framework, experiments are done on both traditional and SDN datasets; the findings demonstrate a significant improvement in classification accuracy.

Wastewater treatment is considered the most important process for reducing pollutants in wastewater to levels that nature can cope with. At many sewages treatment plants, industrial wastes cause more difficulties in the treatment process than any other single problem where the plant operators have to deal with. These plants may not be designed to handle these types of wastes and the accelerated deterioration of sewage treatment plant structures. In this paper, we propose a new industrial IoT cloud-based model for real-time wastewater monitoring and controlling. The proposed system monitors the power of hydrogen (pH) and temperature parameters from the wastewater inlet that will be treated in the wastewater treatment plant, thereby avoiding impermissible industrial wastewater that the plant cannot handle. The system collects and uploads real-time sensor readings to the cloud via an IIoT Wi-Fi Module. Additionally, it reports observed or identified unexpected industrial wastewater inlets via SMS notifications and alarms and controls the valves of the gates. This is needed to change the path of the water to the industrial wastewater treatment plant that can treat this type of wastes. Experimental work shows the effectiveness of the proposed system compared to related work.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS). The growth of preventive processes against DDoS attacks has prompted IoT professionals and security experts to focus on this topic. Due to the increasing prevalence of DDoS attacks, some methods for distinguishing different types of DDoS attacks based on individual network features have become hard to implement. Additionally, monitoring traffic pattern changes and detecting DDoS attacks with accuracy are urgent and necessary. In this paper, using Modified Whale Optimization Algorithm (MWOA) feature extraction and Hybrid Long Short Term Memory (LSTM), shown that DDoS attack detection methods can be developed and tested on various datasets. The MWOA technique, which is used to optimize the weights of the LSTM neural network to reduce prediction errors in the hybrid LSTM algorithm, is used. Additionally, MWOA can optimally extract IP packet features and identify DDoS attacks with the support of MWOA-LSTM model. The proposed MWOA-LSTM framework outperforms standard support vector machines (SVM) and Genetic Algorithm (GA) as well as standard methods for detecting attacks based on precision, recall and accuracy measurements.

Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners’ responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users’ ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust values. According to the results of the experimental work, it is shown that the proposed approaches outperform related work. Besides, it is shown that the use of machine learning provides slightly better performance than the computing model. Both proposed approaches were found successful in degrading malicious ratings without the need for more complex algorithms.

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach.

The Internet of Things refers to the type of network that connects anything to the Internet on the basis of the protocols stipulated by the information sensing equipment for the exchange of information and communication in order to achieve smart recognition, positioning, tracing, monitoring and administration. Movable harbor crane (MHC) is a major part of logistics services. Logistics is the management of the flow of goods between the point of origin and the point of consumption in order to meet the requirements of customers or corporations. MHC is mainly used for the loading and unloading of ships. This paper presents recommendations and practices to be used in the future of the smart industry internet (IoSI) and explores the different applications of smart sensor networks in the movable harbor crane. The main objective of the current paper is to develop a conceptual framework for monitoring and controlling harbor movable crane using a cloud on the IoT-PLC control system. Experimental results show that the proposed system is applicable and effective.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

Sensorless Brushless DC (BLDC) motors have many advantages because of their greet characteristics which make them an interested field of research home and abroad. One of the biggest issues which face BLDC motors is the rotor position detection, as they are electrically commutated the rotor position must be detected with or without sensors. The position detection is used for commutation, speed measurement and speed control. The traditional method for BLDC motor rotor position detection which depends on Hall sensors has lots of problems that decrease the system reliability, so sensorless BLDC motor drives are extensively used. The sensorless position detection methods mostly use Zero Crossing Point (ZCP) detection techniques which depend on motor back EMF detection. In this paper, a suggested method of ZCP detection for speed measurement is presented for BLDC motor, to decrease driver cost and increase system reliability, also the ZCP detection method can be for commutation and speed measurement. Simulation results are shown to prove system effectiveness

Alzheimer's disease is the extremely popular cause of dementia that causes memory loss. People who have Alzheimer's disease suffer from a disorder in neurodegenerative which leads to loss in many brain functions. Nowadays researchers prove that early diagnosis of the disease is the most crucial aspect to enhance the care of patients' lives and enhance treatment. Traditional approaches for diagnosis of Alzheimer's disease (AD) suffers from long time with lack both efficiency and the time it takes for learning and training. Lately, deep-learning-based approaches have been considered for the classification of neuroimaging data correlated to AD. In this paper, we study the use of the Convolutional Neural Networks (CNN) in AD early detection, VGG-16 trained on our datasets is used to make feature extractions for the classification process. Experimental work explains the effectiveness of the proposed approach.

The need to use a wide variety of online services is rising every day in today's digital age. In order to authenticate and authorize users to access services, identity management is essential. To solve several problems such as fragment identity repositories, aim for identity theft and data breaches and identity correlation, the evolution of identity models across siloes, centralized and presently self-sovereign identity based on distributed ledger is emerging. Self-sovereign identity returns the power to the proprietor of the identity and puts the user at the core of the model. In this paper, we propose a framework to identify scam bank service calls and avoid possible loss of personal accounts. Our framework is based on Hyperledger Indy, a public and permissioned Distributed Ledger Technology. The proposed framework strengthens the Know Your Customer process and enhances the trust of third party agencies delegated by banks to deliver many financial items.