Sustainability

Editorial

Jump to: Research, Review

5 pages, 163 KiB

Open AccessEditorial

Machine Learning, Data Mining, and IoT Applications in Smart and Sustainable Networks

by Muhammad Shafiq, Amjad Ali, Farman Ali and Jin-Ghoo Choi

Sustainability 2024, 16(18), 8059; https://doi.org/10.3390/su16188059 - 14 Sep 2024

Viewed by 1379

Abstract

The smart and sustainable networks require highly connected systems that can improve their operational performance, reduce environmental impact, and increase functional efficiency [...] Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

Research

Jump to: Editorial, Review

18 pages, 3112 KiB

Open AccessArticle

Dynamic Key Extraction Technique Using Pulse Signal and Lightweight Cryptographic Authentication Scheme for WBAN

by Shafiq Ahmad, Zia ur Rehman, Saud Altaf, Mazen Zaindin, Shamsul Huda, Muhammad Haroon and Sofia Iqbal

Sustainability 2022, 14(21), 14625; https://doi.org/10.3390/su142114625 - 7 Nov 2022

Cited by 1 | Viewed by 1548

Abstract

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives [...] Read more.

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

18 pages, 6289 KiB

Open AccessArticle

Manta Ray Foraging Optimization with Transfer Learning Driven Facial Emotion Recognition

by Anwer Mustafa Hilal, Dalia H. Elkamchouchi, Saud S. Alotaibi, Mohammed Maray, Mahmoud Othman, Amgad Atta Abdelmageed, Abu Sarwar Zamani and Mohamed I. Eldesouki

Sustainability 2022, 14(21), 14308; https://doi.org/10.3390/su142114308 - 2 Nov 2022

Cited by 9 | Viewed by 1967

Abstract

Recently, facial expression-based emotion recognition techniques obtained excellent outcomes in several real-time applications such as healthcare, surveillance, etc. Machine-learning (ML) and deep-learning (DL) approaches can be widely employed for facial image analysis and emotion recognition problems. Therefore, this study develops a Transfer Learning [...] Read more.

Recently, facial expression-based emotion recognition techniques obtained excellent outcomes in several real-time applications such as healthcare, surveillance, etc. Machine-learning (ML) and deep-learning (DL) approaches can be widely employed for facial image analysis and emotion recognition problems. Therefore, this study develops a Transfer Learning Driven Facial Emotion Recognition for Advanced Driver Assistance System (TLDFER-ADAS) technique. The TLDFER-ADAS technique helps proper driving and determines the different types of drivers’ emotions. The TLDFER-ADAS technique initially performs contrast enhancement procedures to enhance image quality. In the TLDFER-ADAS technique, the Xception model was applied to derive feature vectors. For driver emotion classification, manta ray foraging optimization (MRFO) with the quantum dot neural network (QDNN) model was exploited in this work. The experimental result analysis of the TLDFER-ADAS technique was performed on FER-2013 and CK+ datasets. The comparison study demonstrated the promising performance of the proposed model, with maximum accuracy of 99.31% and 99.29% on FER-2013 and CK+ datasets, respectively. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

26 pages, 4300 KiB

Open AccessArticle

Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

by Mukesh Kumar, Saurabh Singhal, Shashi Shekhar, Bhisham Sharma and Gautam Srivastava

Sustainability 2022, 14(21), 13998; https://doi.org/10.3390/su142113998 - 27 Oct 2022

Cited by 43 | Viewed by 5881

Abstract

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds [...] Read more.

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

13 pages, 430 KiB

Open AccessArticle

Evaluation of Online Communities for Technology Foresight: Data-Driven Approach Based on Expertise and Diversity

by Youngjun Kim and Changho Son

Sustainability 2022, 14(20), 13040; https://doi.org/10.3390/su142013040 - 12 Oct 2022

Cited by 1 | Viewed by 1703

Abstract

This study proposes a framework for selecting and validating data sources for public-based technology foresight. In other words, it finds out which of the many online communities have valuable data sources. Specifically, we evaluate the usefulness of text data from online communities for [...] Read more.

This study proposes a framework for selecting and validating data sources for public-based technology foresight. In other words, it finds out which of the many online communities have valuable data sources. Specifically, we evaluate the usefulness of text data from online communities for technology foresight in terms of expertise and diversity. To this end, not only is a bibliographic analysis using metadata conducted, but also, topic modeling techniques for a semantic analysis of texts are utilized. As a case study, we selected 20 candidate communities where discussions and predictions related to technology are made and applied newly proposed metrics. As a contribution of this study, it is expected that it will provide a basis for public participation in technology foresight, not only leaving it to a few experts. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

15 pages, 3511 KiB

Open AccessArticle

A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women

by Keshav Kaushik, Akashdeep Bhardwaj, Salil Bharany, Naif Alsharabi, Ateeq Ur Rehman, Elsayed Tag Eldin and Nivin A. Ghamry

Sustainability 2022, 14(19), 11947; https://doi.org/10.3390/su141911947 - 22 Sep 2022

Cited by 23 | Viewed by 2837

Abstract

One of the most common types of cancer in women is cervical cancer, a disease which is the most prevalent in poor nations, with one woman dying from it every two minutes. It has a major impact on the cancer burden in all [...] Read more.

One of the most common types of cancer in women is cervical cancer, a disease which is the most prevalent in poor nations, with one woman dying from it every two minutes. It has a major impact on the cancer burden in all cultures and economies. Clinicians have planned to use improvements in digital imaging and machine learning to enhance cervical cancer screening in recent years. Even while most cervical infections, which generate positive tests, do not result in precancer, women who test negative are at low risk for cervical cancer over the next decade. The problem is determining which women with positive HPV test results are more likely to have precancerous alterations in their cervical cells and, as a result, should have a colposcopy to inspect the cervix and collect samples for biopsy, or who requires urgent treatment. Previous research has suggested techniques to automate the dual-stain assessment, which has significant clinical implications. The authors reviewed previous research and proposed the cancer risk prediction model using deep learning. This model initially imports dataset and libraries for data analysis and posts which data standardization and basic visualization was performed. Finally, the model was designed and trained to predict cervical cancer, and the accuracy and performance were evaluated using the Cervical Cancer dataset. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

16 pages, 29678 KiB

Open AccessArticle

Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection

by Fang Xie, Hao Luo, Shaoqian Li, Yingchun Liu and Baojun Lin

Sustainability 2022, 14(15), 9277; https://doi.org/10.3390/su14159277 - 28 Jul 2022

Cited by 4 | Viewed by 2388

Abstract

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous [...] Read more.

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

22 pages, 3783 KiB

Open AccessArticle

Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)

by Nida Aslam, Irfan Ullah Khan, Samiha Mirza, Alanoud AlOwayed, Fatima M. Anis, Reef M. Aljuaid and Reham Baageel

Sustainability 2022, 14(12), 7375; https://doi.org/10.3390/su14127375 - 16 Jun 2022

Cited by 31 | Viewed by 5181

Abstract

With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is [...] Read more.

With the expansion of the internet, a major threat has emerged involving the spread of malicious domains intended by attackers to perform illegal activities aiming to target governments, violating privacy of organizations, and even manipulating everyday users. Therefore, detecting these harmful domains is necessary to combat the growing network attacks. Machine Learning (ML) models have shown significant outcomes towards the detection of malicious domains. However, the “black box” nature of the complex ML models obstructs their wide-ranging acceptance in some of the fields. The emergence of Explainable Artificial Intelligence (XAI) has successfully incorporated the interpretability and explicability in the complex models. Furthermore, the post hoc XAI model has enabled the interpretability without affecting the performance of the models. This study aimed to propose an Explainable Artificial Intelligence (XAI) model to detect malicious domains on a recent dataset containing 45,000 samples of malicious and non-malicious domains. In the current study, initially several interpretable ML models, such as Decision Tree (DT) and Naïve Bayes (NB), and black box ensemble models, such as Random Forest (RF), Extreme Gradient Boosting (XGB), AdaBoost (AB), and Cat Boost (CB) algorithms, were implemented and found that XGB outperformed the other classifiers. Furthermore, the post hoc XAI global surrogate model (Shapley additive explanations) and local surrogate LIME were used to generate the explanation of the XGB prediction. Two sets of experiments were performed; initially the model was executed using a preprocessed dataset and later with selected features using the Sequential Forward Feature selection algorithm. The results demonstrate that ML algorithms were able to distinguish benign and malicious domains with overall accuracy ranging from 0.8479 to 0.9856. The ensemble classifier XGB achieved the highest result, with an AUC and accuracy of 0.9991 and 0.9856, respectively, before the feature selection algorithm, while there was an AUC of 0.999 and accuracy of 0.9818 after the feature selection algorithm. The proposed model outperformed the benchmark study. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

33 pages, 2167 KiB

Open AccessArticle

A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions

by Patricia Franco, José M. Martínez, Young-Chon Kim and Mohamed A. Ahmed

Sustainability 2022, 14(8), 4639; https://doi.org/10.3390/su14084639 - 13 Apr 2022

Cited by 7 | Viewed by 3109

Abstract

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control [...] Read more.

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control plane representing the decision-making process. We survey the main technologies and techniques implemented in the recent years for each of the stages, reviewing and identifying the cutting-edge challenges that the research community are currently facing. For the Acquisition part, we define a metering device according to the IEC TS 63297:2021 Standard. We analyze the communication infrastructure as part of beyond 2030 communication era (5G and 6G), and discuss the Analytics stage as the cyber part of the CPS-based HEMS. To conclude, we present a case study in which, using real data collected in an experimental environment, we validate proposed architecture of HEMS in monitoring tasks. Results revealed an accuracy of 99.2% in appliance recognition compared with the state-of-the-art proposals. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

14 pages, 6224 KiB

Open AccessArticle

Advanced Authentication Scheme with Bio-Key Using Artificial Neural Network

by Zia ur Rehman, Saud Altaf, Shafiq Ahmad, Mejdal Alqahtani, Shamsul Huda and Sofia Iqbal

Sustainability 2022, 14(7), 3950; https://doi.org/10.3390/su14073950 - 26 Mar 2022

Cited by 1 | Viewed by 2413

Abstract

The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as [...] Read more.

The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

14 pages, 11938 KiB

Open AccessArticle

An Improved Binomial Distribution-Based Trust Management Algorithm for Remote Patient Monitoring in WBANs

by Sunny Singh, Muskaan Chawla, Devendra Prasad, Divya Anand, Abdullah Alharbi and Wael Alosaimi

Sustainability 2022, 14(4), 2141; https://doi.org/10.3390/su14042141 - 14 Feb 2022

Cited by 7 | Viewed by 2555

Abstract

A wireless body area network (WBAN) is a technology that is widely employed in the medical sector. It is a low-cost network that allows for mobility and variation. It can be used for long-distance, semiautonomous remote monitoring without interfering with people’s regular schedules. [...] Read more.

A wireless body area network (WBAN) is a technology that is widely employed in the medical sector. It is a low-cost network that allows for mobility and variation. It can be used for long-distance, semiautonomous remote monitoring without interfering with people’s regular schedules. Detection devices are embedded in the human body in a simple WBAN configuration to continuously screen physiological boundaries or critical pointers. Confidence among shareholders (for example, medical care suppliers, clients, and medical teachers) is recognized as an essential achievement factor for data stream reliability in such an organization. Given the inherent characteristics of remote locations, it is critical to exercise confidence and security when conducting remote comprehension testing. In the present scenario, WBAN has majorly contributed towards healthcare and its application in medical services. Solid correspondence systems are frequently used to address trust and security concerns on WBANs. In terms of purpose, we present in this study a communication approach built on trust to protect the WBAN’s integrity and confidentiality. For ensuring authenticity, an enhanced bilingual distribution-based trust-management system (PDATMS) approach is used, while a cryptographic system is used to maintain anonymity. A MATLAB simulator is used to evaluate the performance of the recommended program. The recommended approach, according to the release information, improves accuracy by 96%, service delivery rate by 99%, throughput by 99%, as well as confidence, while reducing average latency. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

29 pages, 6724 KiB

Open AccessArticle

Trends and Challenges Regarding Cyber Risk Mitigation by CISOs—A Systematic Literature and Experts’ Opinion Review Based on Text Analytics

by Moti Zwilling

Sustainability 2022, 14(3), 1311; https://doi.org/10.3390/su14031311 - 24 Jan 2022

Cited by 5 | Viewed by 4504

Abstract

Background: Cyber security has turned out to be one of the main challenges of recent years. As the variety of system and application vulnerabilities has increased dramatically in recent years, cyber attackers have managed to penetrate the networks and infrastructures of larger numbers [...] Read more.

Background: Cyber security has turned out to be one of the main challenges of recent years. As the variety of system and application vulnerabilities has increased dramatically in recent years, cyber attackers have managed to penetrate the networks and infrastructures of larger numbers of companies, thus increasing the latter’s exposure to cyber threats. To mitigate this exposure, it is crucial for CISOs to have sufficient training and skills to help them identify how well security controls are managed and whether these controls offer the company sufficient protection against cyber threats, as expected. However, recent literature shows a lack of clarity regarding the manner in which the CISOs’ role and the companies’ investment in their skills should change in view of these developments. Therefore, the aim of this study is to investigate the relationship between the CISOs’ level of cyber security-related preparation to mitigate cyber threats (and specifically, the companies’ attitudes toward investing in such preparation) and the recent evolution of cyber threats. Methods: The study data are based on the following public resources: (1) recent scientific literature; (2) cyber threat-related opinion news articles; and (3) OWASP’s reported list of vulnerabilities. Data analysis was performed using various text mining methods and tools. Results: The study’s findings show that although the implementation of cyber defense tools has gained more serious attention in recent years, CISOs still lack sufficient support from management and sufficient knowledge and skills to mitigate current and new cyber threats. Conclusions: The research outcomes may allow practitioners to examine whether the companies’ level of cyber security controls matches the CISOs’ skills, and whether a comprehensive security education program is required. The present article discusses these findings and their implications. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

20 pages, 2036 KiB

Open AccessArticle

The Structural Relationship between Service Quality and Sustainable Use Intention of Voice Search Technology in Korea

by Jaepil Yoo

Sustainability 2021, 13(24), 14026; https://doi.org/10.3390/su132414026 - 19 Dec 2021

Cited by 2 | Viewed by 2697

Abstract

Voice search based on artificial intelligence is the fastest means of searching for information and can be easily used in a very familiar way by ordinary users without separate education or learning. Voice bot’s voice search can interact on the same level as [...] Read more.

Voice search based on artificial intelligence is the fastest means of searching for information and can be easily used in a very familiar way by ordinary users without separate education or learning. Voice bot’s voice search can interact on the same level as face-to-face communication and provide customized services optimized for users. It is most important for these new technologies to develop from a non-face-to-face social structure caused by COVID-19 to a long-term sustainable technology rather than short-term development. Therefore, the purpose of this study is to empirically verify the structural relationship between the quality, interactivity, and consumers’ sustainable use intentions for voice search services called ‘voice bots’ in Korea, an advanced country of computer science and technology. A survey was conducted on Korean consumers aged 20 or older who use voice search services, and the following main results were derived. First, the playfulness, certainty, and empathy of the ‘voice bot’ have a positive effect on the interactivity with the ‘voice bot’. Second, interactivity with the ‘voice bot’ has a positive effect on consumers’ sustainable use intention. Third, the playfulness and certainty of ‘voice bot’ have a positive effect on consumers’ sustainable use intention. Fourth, the playfulness, certainty, and empathy of the ‘voice bot’ have a positive effect on consumers’ sustainable use intention through interactivity with the ‘voice bot’. This study is meaningful in that it empirically identified the importance of interactivity by deriving the service quality factors required for sustainable use of voice search services, one of the new innovative technologies. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

26 pages, 9794 KiB

Open AccessArticle

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

by Adnan Yousaf, Rao Muhammad Asif, Mustafa Shakir, Ateeq Ur Rehman, Fawaz Alassery, Habib Hamam and Omar Cheikhrouhou

Sustainability 2021, 13(22), 12693; https://doi.org/10.3390/su132212693 - 16 Nov 2021

Cited by 27 | Viewed by 3134

Abstract

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from [...] Read more.

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

19 pages, 960 KiB

Open AccessArticle

Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

by Abdullah Alharbi, Adil Hussain Seh, Wael Alosaimi, Hashem Alyami, Alka Agrawal, Rajeev Kumar and Raees Ahmad Khan

Sustainability 2021, 13(22), 12337; https://doi.org/10.3390/su132212337 - 9 Nov 2021

Cited by 12 | Viewed by 3162

Abstract

Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function [...] Read more.

Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

21 pages, 2503 KiB

Open AccessArticle

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

by Bashir Khan Yousafzai, Sher Afzal Khan, Taj Rahman, Inayat Khan, Inam Ullah, Ateeq Ur Rehman, Mohammed Baz, Habib Hamam and Omar Cheikhrouhou

Sustainability 2021, 13(17), 9775; https://doi.org/10.3390/su13179775 - 31 Aug 2021

Cited by 79 | Viewed by 5412

Abstract

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic [...] Read more.

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

19 pages, 613 KiB

Open AccessArticle

Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

by Naeem Ahmed Mahoto, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon and Adel Sulaiman

Sustainability 2021, 13(16), 8900; https://doi.org/10.3390/su13168900 - 9 Aug 2021

Cited by 6 | Viewed by 2827

Abstract

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery [...] Read more.

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

20 pages, 4262 KiB

Open AccessArticle

An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

by Adnan Yousaf, Rao Muhammad Asif, Mustafa Shakir, Ateeq Ur Rehman and Mohmmed S. Adrees

Sustainability 2021, 13(11), 6199; https://doi.org/10.3390/su13116199 - 31 May 2021

Cited by 35 | Viewed by 3321

Abstract

Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market [...] Read more.

Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

22 pages, 2858 KiB

Open AccessArticle

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

by Ahmad B. Hassanat, Sami Mnasri, Mohammed A. Aseeri, Khaled Alhazmi, Omar Cheikhrouhou, Ghada Altarawneh, Malek Alrashidi, Ahmad S. Tarawneh, Khalid S. Almohammadi and Hani Almoamari

Sustainability 2021, 13(9), 4888; https://doi.org/10.3390/su13094888 - 27 Apr 2021

Cited by 19 | Viewed by 4006

Abstract

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, [...] Read more.

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

20 pages, 2039 KiB

Open AccessArticle

A New Efficient Architecture for Adaptive Bit-Rate Video Streaming

by Muhammad Hamza Bin Waheed, Faisal Jamil, Amir Qayyum, Harun Jamil, Omar Cheikhrouhou, Muhammad Ibrahim, Bharat Bhushan and Habib Hmam

Sustainability 2021, 13(8), 4541; https://doi.org/10.3390/su13084541 - 19 Apr 2021

Cited by 8 | Viewed by 5487

Abstract

The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with [...] Read more.

The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out for the factors impacting QoE, and their behavior is evaluated. The statistical data is taken from real architecture and analysis. Likewise, we have compared the response time and throughput with the varying segment size in adaptive bitrate video streaming. Moreover, resource usage is also analyzed by incorporating the effect of CPU consumption and energy consumption over segment size, which will be counted as effective efforts for sustainable development of multimedia systems. The proposed architecture is validated and indulged as a core component for video streaming based on the use case of a Mobile IPTV solution for 4G/LTE Users. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

31 pages, 2027 KiB

Open AccessReview

Security and Privacy Issues in Medical Internet of Things: Overview, Countermeasures, Challenges and Future Directions

by Mohamed Elhoseny, Navod Neranjan Thilakarathne, Mohammed I. Alghamdi, Rakesh Kumar Mahendran, Akber Abid Gardezi, Hesiri Weerasinghe and Anuradhi Welhenge

Sustainability 2021, 13(21), 11645; https://doi.org/10.3390/su132111645 - 21 Oct 2021

Cited by 69 | Viewed by 8822

Abstract

The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this [...] Read more.

The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

19 pages, 3087 KiB

Open AccessReview

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

by Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer and Kuburat Oyeranti Adefemi Alimi

Sustainability 2021, 13(17), 9597; https://doi.org/10.3390/su13179597 - 26 Aug 2021

Cited by 34 | Viewed by 5203

Abstract

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote [...] Read more.

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works. Full article

(This article belongs to the Special Issue Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Machine Learning, Data Mining and IoT Applications in Smart and Sustainable Networks

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (22 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI