Integration of Building Information Modeling and Project Management Process

Haretha; Mohammed

doi:https://doi.org/10.54216/IJBES.080101

Journal of Intelligent Systems and Internet of Things | 2024

Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems

Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches.

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R. Rajkumar mail -

Dınesh Valluru mail -

Siva Satya Sreedhar P. mail -

N. Ramshankar mail -

Sujatha S. mail -

Somasundaram R. mail -

M. Sudha mail -

S. Navaneethan mail

link https://doi.org/10.54216/JISIoT.110209

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Analyzing Social Media Data to Understand Long-Term Crisis Management Challenges of COVID-19

In the past three years, social media has had a significant impact on our lives, including crisis management. The COVID-19 pandemic highlighted the importance of accurate information and exposed the spread of false information. This paper specifically examines the COVID-19 crisis and analyzes relevant literature to provide insights for national authorities and organizations. Utilizing social media data for crisis management poses challenges due to its unstructured nature. To overcome this, the paper proposes a comprehensive method that addresses all aspects of long-term crisis management. This method relies on labeled and structured information for accurate sentiment analysis and classification. An automated approach is presented to annotate and classify tweet texts, reducing manual labeling and improving classifier accuracy. The framework involves generating topics using Latent Dirichlet Allocation (LDA) and ranking them with a new algorithm for data annotation. The labeled text is transformed into feature representation using Bert embeddings, which can be utilized in deep learning models for categorizing textual data. The primary aim of this paper is to offer valuable insights and resources to researchers studying crisis management through social media literature, with a specific focus on high-accuracy sentiment analysis.

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Ali S. Abed Al Sailawi mail -

Mohammad Reza Kangavari mail

link https://doi.org/10.54216/FPA.140219

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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International Journal of Neutrosophic Science |

Energy of Fuzzy, Intuitionistic fuzzy, and Neutrosophic graphs in decision making-A literature review

This review of the literature delves into the complex interplay between energy measures and decision-making processes in the domains of fuzzy graphs, intuitionistic fuzzy graphs, and neutrosophic graphs. In graph theory, energy is a key quantity that is used to measure structural properties and evaluate decision model dynamics. The research methodically examines the theoretical underpinnings, computational techniques, and practical applications of energy measures in contexts involving decision-making, taking into account the special features brought forth by fuzzy, intuitionistic fuzzy, and neutrosophic graph models. This review attempts to provide a thorough understanding for researchers and practitioners looking to use energy measures for efficient decision support in the setting of uncertainty contained within these specific graph topologies by synthesizing prior research.

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Journal of Intelligent Systems and Internet of Things |

VIDEO BASED VIOLENCE DETECTION USING DEEP LEARNING CNN-CHA-SPA DOUBLE ATTENTION MECHANISM WITH MOSAICKING

Violence detection refers to the use of various technologies and methods to identify, keep track of, and react to instances of physical or verbal aggressiveness, threatening conduct, or violent acts. Security, public safety, and online content filtering are just a few areas where this use is vital. Due of the differences in the human body, it is challenging to capture more accurate and discriminative features for video-based violence detection. Automatically spotting aggressive behaviour in places with video surveillance, such train stations, gyms, and psychiatric facilities, is crucial. As a result, this research focuses on creating a violence prediction system with improved feature extraction and classification techniques while researching various and efficient feature extraction techniques. Constructing an improvised violence detection system has some difficulties. Deep neural self-attention and CNN feature extraction methods are used to determine if a video contains violent content or not in order to solve the aforementioned complexity, such as focusing on the types of attacks and improving the accuracy of violence detection. The Proposed Method CNN-CHA-SPA Double Attention Mechanism with CNN helps to extract the frames correctly and detect the video is Violent or not. Here, a cutting-edge deep learning approach using video mosaicking is suggested. The extracted images from the video are combined with these mosaic images in the preprocessing stage, which offer a more thorough perspective of the scene and which will aid in accurately extracting the feature and helps to obtain time consistent outcomes and on the other hand improve the performance of the algorithm. This proposed mechanism provides the accurate result compared to the other mechanisms available.

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International Journal of Neutrosophic Science | 2024

Integrated Neutrosophic methodology and Machine Learning Models for Cybersecurity Risk Assessment: An exploratory study

  Information technology security, or Cybersecurity, guards against hostile cyberattacks on computers, mobile devices, servers, electronic systems, and networks. Cybersecurity risks have been a significant concern for any vital digital infrastructure in recent years, and different online cyberattacks are also becoming a significant problem for society. Consequently, it's critical to adopt technology created to provide cybersecurity. However, one should consider the associated hazards while selecting among Cybersecurity systems. We have developed a multi-criteria decision-making (MCDM) approach based on a single-valued neutrosophic set (SVNS). This allows specialists more latitude in assessing the criteria and alternatives using language and overcoming uncertain information. The VIKOR is an MCDM methodology used to rank the other options. The VIKOR method is integrated with the neutrosophic set. There are 18 criteria, and 10 alternatives are used in this study. The sensitivity analysis and comparative analysis are conducted in this study. The sensitivity analysis results show the alternatives' rank is stable under different cases. The comparative analysis compares the suggested method with other MCDM methods. The comparative analysis shows the suggested method was effective compared with other MCDM methods. Machine learning methods predict the type of attack in Cybersecurity. This study uses Three machine learning methods: decision tree, random forest, and support vector machine.

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Ali Alqazzaz mail

link https://doi.org/10.54216/IJNS.230317

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

Fusion: Practice and Applications | 2024

Hybrid Fusion of Lightweight Security Frameworks Using Data Mining Approach in IoT

The rapid adoption of the Internet of Things throughout healthcare and smart city construction has led to a rise in networked devices and security issues. This work suggests new techniques to improve IoT safety and maximise computing resources. We develop a complete security architecture integrating lightweight cryptography, blockchain, machine learning anomaly detection, and federated learning. We did so because we know that traditional security measures are inadequate for the Internet of Things. The lightweight cryptographic algorithm (LCA) provides efficient encryption and decryption, making it ideal for low-resource Internet of Things devices. Twenty processes comprise the LCA design. These operations include key generation, data encryption, digital signatures, and integrity checking. These procedures secure IoT data transfers. ADML detects anomalies in encrypted Internet of Things data using machine learning. This approach may identify security issues better. To keep up with data trends, this method extracts features, trains models, and updates them. Blockchain-based data integrity (BDI) is the third element. Blockchain ensures that Internet of Things data is reliable and full. BDI developed an immutable ledger solution to increase IoT data security and dependability. This data integrity system generates blocks, hashes, confirms blocks, and updates the blockchain. Fourth, FLIoT (Federated Learning for the Internet of Things) emphasises data privacy and collaborative model training across IoT devices. Foundation for the Internet of Things (FIoT) protocols and standards aim to increase IoT devices' collective intelligence while safeguarding users' privacy. It includes local model training, model aggregation, and the latest global model distribution. Our work also uses Secure Multi-party Computation (SMC) to analyse data more thoroughly and continuously, addressing online transaction cybersecurity issues. The framework outperforms the current state of the art in memory use, energy consumption, anomaly detection accuracy and precision, and encryption and decryption time. The "Hybrid Fusion Framework" combines lightweight cryptographic algorithms with federated learning, machine learning, blockchain technology, and other similar technologies to provide an effective, adaptable, and affordable IoT security solution.

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Abhishek Kumar mail -

Samta Jain Goyal mail -

Sumit Kumar mail -

Hitesh Kumar Sharma mail

link https://doi.org/10.54216/FPA.140220

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Artificial Intelligence and Metaheuristics | 2024

Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning

This paper deals with a pivotal part of educational data analytics, aiming to increase the accuracy and interpretability of student performance prediction models. The cornerstone of our method is the innovative application of binary waterwheel plant algorithm bWWPA in the feature selection. As we can see, an essential part of any model is the predicted values, which correctly define all the characteristics of this model. Practically, we begin with solid data pre-processing, which incorporates data cleaning and missing values, duplicate removal, and data transformation in order to get model input as optimally as possible. Preceding the application of bWWPA, we employ an ensemble of regression machine learning models. Set up a baseline for predictive capability, getting initial outcomes with an average Mean Squared Error (MSE) of 0.064. The following feature selection phase proceeds, showing the algorithm. Ability to recognize important elements and, as a result, improve model effectiveness and explain power. The comparative analyses after feature selection point to refined gains in the model, and the performance is reporting a lower MSE of 0.032 with the refined models. These findings, methodologically, add to student performance prediction. Accordingly, it emphasizes the decisive status of feature selection in improving models. The paper's significance extends to teachers, institutions, and researchers, giving insights into more precise and relevant student success-supporting interventions.

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Faris H. Rizk mail -

Mahmoud Elshabrawy mail -

Basant Sameh mail -

Karim Mohamed mail -

Ahmed Mohamed Zaki mail

link https://doi.org/10.54216/JAIM.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2024

Exploring Predictive Models for Students' Performance in Exams: A Comparative Analysis of Regression Algorithms

Student-centered analysis of academic performance is also the most important aspect in improving education by being able to determine what measures work best, individualized learning approaches, and intervention programs. In this study, we performed a detailed analysis based on the "Students Performance in Exams" dataset and different regression methods to estimate students' grades. We sought to assess the functioning of numerous metrics and determine an optimal model for this task. Our descriptive analysis identified meaningful trends within this dataset, as it includes central factors like 'gender," race/ethnic diversity-based status of a student,' and parental education level based on which the children are catered to by informing them about important lunches and test preparation courses alongside scores in "Math," Readings," Writing" etc. We used a wide range of regression models: XGBoost, CatBoost, GradientBoostingRegressor, etc. Metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Average Marginal Loss were used to assess each model rigorously. Importantly, the XGBoost model gave out an MSE value of 0.028, which was the best among all values obtained from various other models. The superiority of the XGBoost model is supported by the excellent performance that was reported across many metrics. This work can be important for informing educational practitioners and policymakers regarding the best possible accurate and realistic model that would predict the students' outcome results. Educational data analytics incorporating the XGBoost model can be used for the customization of interventions and mapping resource allocation while promoting a results-oriented approach based on data in education. This study is a step towards the accumulation of knowledge on educational data analytics. It can serve as a background for further research aimed at improving predictive models regarding student performance.

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Faris H. Rizk mail -

Ahmed Saleh mail -

Abdulrhman Elgaml mail -

Ahmed Elsakaan mail -

Ahmed Mohamed Zaki mail

link https://doi.org/10.54216/JAIM.070103

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2024

The Applications of Digital Transformation Towards Achieving Sustainable Development Goals: Practical Case Studies in Different Countries of the World

Digital transformation is steadily changing states and organizations, and making them more cutthroat, as well as it offers a few valuable open doors for monetary development and success, as it empowers nations to including, more expanded instructive open doors, widespread admittance to the web and an exhaustive and supportive climate to the improvement interaction. The Communication and Information Technology industry's role in achieving sustainable development is highlighted in this paper, which focuses on the concept of sustainable development. hence, this paper audits the 17 SDGs (supportable advancement objectives) and makes sense of what data innovation has mean for every objective. In addition, we track progress toward the SDGs, existing e-governance initiatives, and big data initiatives in four distinct nations in the world. While in the next part, we significantly review the digital transformation in Egypt and its contribution in achieving sustainable development in Egypt.

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Amira Hassan Abed mail -

Faris H. Rizk mail -

Ahmed Mohamed Zaki mail -

Ahmed M. Elshewey mail

link https://doi.org/10.54216/JAIM.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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International Journal of BIM and Engineering Science | 2024

Integration of Building Information Modeling and Project Management Process

The construction industry has witnessed significant technological advancements in recent years, with Building Information Modelling (BIM) emerging as a pivotal innovation. BIM's integration with Project Management (PM) is recognized as a transformative approach that can potentially enhance efficiency, accuracy, and collaboration within the Architecture, Engineering, and Construction (AEC) industry. This paper explores the integration of BIM and PM, focusing on identifying the challenges and benefits inherent in this convergence. The primary aim is to understand how the synergistic use of BIM and PM can be optimized to improve project outcomes in the construction sector. As the industry evolves, understanding these dynamics becomes crucial for stakeholders aiming to leverage technological advancements for optimal project delivery. The research problem central to this study stems from the observed gaps and challenges in effectively integrating BIM with PM practices. While BIM offers a multidimensional, collaborative framework for construction projects, its full potential is often unrealized due to various implementation challenges. These challenges include technical issues, lack of standardization, and resistance to change within organizational cultures. Recognizing the importance of this integration, the study sets out to answer key research questions revolving around the current state of BIM and PM integration, the main challenges faced, and the potential strategies for effective implementation. The objectives include a detailed analysis of the current practices in BIM implementation, identification of the barriers to effective integration, and proposing a framework that can enhance the synergy between BIM and PM. The methodology employed in this research involves a comprehensive literature review, followed by a survey-based approach to gather data from industry professionals. This data provides insights into the practical aspects of BIM and PM integration and helps in identifying the key factors that influence project success in the context of this integration. This paper contributes to the existing body of knowledge by providing a nuanced understanding of the complexities and potential of integrating BIM and PM. It aims to offer a strategic framework that can guide practitioners and stakeholders in effectively navigating the challenges and leveraging the benefits of this integration for enhanced project performance in the AEC industry.

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Haretha Aljabr mail -

Mohammed Ali mail

link https://doi.org/10.54216/IJBES.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new