Automated Kidney Cancer Classification using White Shark Optimizer with Ensemble Majority Voting Model on Pathology Images

Ashrf

doi:https://doi.org/10.54216/FPA.190230

International Journal of BIM and Engineering Science | 2025

HBIM's Role in the Conservation and Restoration of Archaeological Buildings: Case Study: Omar Al-Khyam Hotel in Damascus

Historic Building Information Modelling (HBIM) has emerged as a critical methodology for preserving cultural heritage by documenting the condition of building materials, assessing the extent and causes of damage, and managing restoration and maintenance activities. By integrating advanced technologies such as thematic mapping and 3D modeling, HBIM offers a comprehensive approach to analyzing and conserving historic structures. This research highlights the significance of HBIM in preserving the integrity and sustainability of heritage buildings, emphasizing its role in maintaining their historical and cultural value. The study focuses on the Omar al-Khiam Hotel in Damascus, an iconic historic building requiring urgent restoration. A detailed photographic survey was conducted using a mobile camera, with images processed through AGISOFT METASHAPE and enhanced using Photoshop. These data were used to create a precise 3D model in EDIFICIUS HBIM software, incorporating detailed assessments of material conditions, including corrosion, damage, leakage, and environmental pollution. Based on this analysis, a restoration and maintenance schedule was developed to guide the rehabilitation process and ensure effective project management. The findings demonstrate the effectiveness of HBIM in providing a dynamic and collaborative platform for heritage conservation. The study underscores the need for integrating diverse data sources and engaging stakeholders in restoration efforts. While HBIM offers significant advantages, challenges such as data precision and software complexity were identified. Future research should focus on enhancing HBIM’s predictive capabilities for long-term material degradation and exploring its application across diverse heritage sites to refine conservation strategies further.

groups

Rasha Daoud mail -

Sonia Ahmad mail -

Khaled Alfahed mail

link https://doi.org/10.54216/IJBES.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Multi-Year Financial Performance Comparison of Banks: Neutrosophic Approach

In this work, a comparison plan of Agrobank and NBU for the financial years 2021, 2022, 2023, and 2024 is provided via neutrosophic approach in terms of indicators of profitability, liquidity, and solvency. The profits of the banks are analyzed through the application of net profit margin, profitability coefficient, absolute liquidity ratio, and solvency ratios. The economic ratios on profitability and liquidity point out that the NBU bank is performing better than the Agrobank but solvency ratios depict that Agrobank is more stabilized than NBU. This framework will avail a relative comparison of the two banks in terms of the opportunities, threats, strengths and weaknesses of each. In this way, findings can improve the understanding of banking industry’s performance in Uzbekistan and provide useful information to policuemakers and researchers. Continuation of the study could include the consideration of factors outside the firm to determine how they affect financial performance.  

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Samandarboy Sulaymanov mail

link https://doi.org/10.54216/IJNS.260225

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Proposed BIM-CMMS Framework for Facility Management in Digital Transformation Era

Digital transformation is crucial for construction projects due to its numerous benefits, including increased productivity and improved collaborative environments. This research discusses the stages, components, and strategies that lead construction projects to digital transformation. Furthermore, it aims to advance the technological process of 3D digitization in built environments and simplify management operations in the construction phase through digital methodologies. To achieve this, an integrated framework combining Building Information Modeling (BIM) and Computerized Maintenance Management Systems (CMMS) applications is proposed. By using these integrated models, facility management is simulated within a 3D environment via a CMMS. The results indicated that digital models and BIM could indeed be integrated through direct linkage mechanisms without compromising the efficiency of information synchronization and management. This 3D representation allowed for a better understanding of dynamics and spatial interactions, facilitating quicker identification of potential issues and more efficient maintenance operations. Therefore, integrating these advanced digital models not only improves operational efficiency, but also enhances collaborative environments. The proposed model represents what is known as a Digital Twin, a comprehensive system that manages all information flows associated with a building throughout its lifecycle.

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Nisren Sharief mail -

Bashar Abd Alnoor mail -

Khaled Al-fahed mail

link https://doi.org/10.54216/IJBES.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Ethics and Data Privacy in BIM

The rapid advancement of Building Information Modeling (BIM) has revolutionized the construction industry, enabling collaborative workflows among architects, engineers, contractors, and clients. However, it has introduced critical ethical and legal challenges related to data ownership, intellectual property rights, and privacy. This thesis explores these issues by analyzing legal frameworks, contractual agreements, and ethical considerations governing BIM data ownership. It examines stakeholder roles, recurring disputes, and the impact of BIM’s collaborative environment, with a focus on global and regional contractual adequacy. Findings reveal frequent conflicts between engineering teams and clients over intellectual property, highlighting the need for explicit contractual provisions and ethical guidelines addressing privacy, consent, and data control. The study proposes actionable recommendations to establish a robust framework for equitable, transparent, and sustainable data management in the construction sector.

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Nura ALHallak mail -

Hassan M. Ali mail -

Mohamed Shaban mail

link https://doi.org/10.54216/IJBES.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Design of Artificial Intelligence-Based Biometric Authentication System using Deepfake Detection Model for Patient Data Privacy Protection and Identity Verification

In biometric applications, deepfake detection is a major field of research, as it is vital to certify the authenticity and integrity of biometric data. The manipulation of biometric information, like facial and fingerprint images, presents a critical attack on patient confidentiality and healthcare security. Deepfake is one of the manipulated digital media, for instance, an image or video of an individual can be substituted with a resemblance of another being. On the other hand, the growth of deepfake technology sets major attacks on biometric security by making hyper-realistic fake individualities that can deploy authentication methods. For deepfake recognition, a vital method in biometric applications utilizes a machine learning (ML) system, mainly deep learning (DL) that might study to differentiate amongst real and fake biometric data. In this manuscript, we present a Design of an Artificial Intelligence-Based Biometric Authentication System for Deepfake Detection with Patient Data Privacy Protection and Identity Verification (AIBADD-PDPPIV) algorithm. The main intention of the AIBADD-PDPPIV model is to deliver a secure and efficient biometric authentication approach that contributes to the advancement of privacy-preserving biometric security in healthcare systems. To accomplish this, the AIBADD-PDPPIV method employs an image preprocessing stage using the adaptive median filter (AMF) to reduce noise and enhance essential biometric features. For feature extraction, the vision transformer (ViT) model can be employed to capture intricate spatial dependencies in biometric images. Moreover, the multi‐head attention mechanism-based bidirectional gated recurrent unit (MA-BiGRU) model is exploited for deepfake detection and authentication processes. Eventually, the hyperparameter tuning process is accomplished through the pelican optimization algorithm (POA) to improve the detection performance of the MA-BiGRU model. To show the improved performance of AIBADD-PDPPIV model, a wide sort of simulations take place and the outcomes are inspected under numerous measures. The comparison study reported the betterment of AIBADD-PDPPIV system under various metrics.

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Louai A. Maghrabi mail

link https://doi.org/10.54216/JCIM.160119

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing NLP Translation Accuracy with Cloud and Edge Computing- (BD-EC-ETS)

The exponential growth of the Internet, distributed computing, and search engines has led to a steady improvement in the quality of Natural languge processing translation platforms that rely on these technologies. However, reusing the corpus is a challenge in the conventional translation setting. Other issues that translators frequently face include a tight cycle, challenging software manipulation, difficult internal and external cooperation, and inconsistent translation style. From this, the Natural languge processing Translation System (ETS) emerges incognito, with the primary goal of assisting all users in increasing translation efficiency and decreasing translation costs. This work uses research on Intelligent Big Data systems and Edge Computing to an Natural languge processing Translation System (BD-EC-ETS), which significantly advances the field of Natural languge processing translation with higher accuracy. With the Internet of Things and big data techniques, this article will examine a cutting-edge system for Natural languge processing translation software, identify its flaws and shortcomings, and provide data research to inform a system upgrade.The study focuses on Natural languge processing translation systemsto enhance the quality of the system's output translations. This paperexamines the current interactive language translation systems, focusing on those that use phrase models and get their information from edge computing enabled by the Internet of Things. Machine-efficient and cost-effective translation has emerged as a solution to such problems; researchers have focused on enhancing the Natural languge processing translation system's output quality via BD-EC-ETS. The system's outstanding performance in improving Natural languge processing translation accuracy and recall rate has been shown. Compared to the current Natural languge processing translation system, the accuracy improves by over 22% with fewer iterations and by as much as 100% with 80 iterations.

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Mohanaprakash T. A. mail -

Muthalakshmi M. mail -

Vijaya A. mail -

Selvakumari S. mail -

E. Ajitha mail -

Naveen P. mail

link https://doi.org/10.54216/JCIM.160118

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Using Neutrosophic Soft Set to predict Higher Education Academic Performance

Neutrosophic Logic is a neonate research field in which every proposition is assessed to have the proportion (percentage) of truth in a sub-set T, the proportion of indeterminacy in a sub-set I, and the proportion of falsity in a sub-set F. Neutrosophic set (NS) is effectively implemented for undetermined data processing and establishes benefits for handling the indeterminacy data. In the academic industries, early performance prediction of students is significant to the academic community so strategic interference might be planned before students attain the final semester. Forecasting the performance of students has turned into a challenging task owing to the rising number of data in educational procedures. The educational data mining (EDM) models are involved in extracting a pattern to explore hidden data from educational information. Currently, Machine learning (ML) and Artificial intelligence (AI) are implemented in numerous domains generally in the field of education to evaluate and analyze several features of educational datasets gathered from many educational institutions. This study develops a Leveraging Generalized Possibility Neutrosophic Soft Set with Feature Selection for Accurate Students’ Academic Performance Prediction Model (GPNSSFS-SAPPM). The intention of the proposed GPNSSFS-SAPPM system relies on improving the prediction model of students’ higher education performance using metaheuristic optimization algorithms.  The data pre-processing model is employed at first by applying mean normalization for converting input data into a suitable format. In addition, the golf optimization algorithm (GOA) is exploited for the feature selection process. Followed by, the classification process is done by generalized possibility neutrosophic soft set (GPNSS). At last, the parameter tuning process is performed through henry gas solubility optimization (HGSO) algorithm to improve the classification performance of the GPNSS classifier. A wide-ranging experimentation was performed to prove the performance of the GPNSSFS-SAPPM method. The experimental results specified that the GPNSSFS-SAPPM model underlined advancement over other recent techniques.

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Sally Afchal mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/IJNS.260302

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

AI and Machine Learning for Breast Cancer Diagnosis Using Histopathology and Clinical Decision Systems

The diagnosis of breast cancer depends on histopathology for precise and trusted evaluation between malignant tumor cells and benign cells. The analysis demands significant time and creates additional room for human errors. A deep learning approach for computer-aided diagnosis (CAD) establishes techniques to enhance the classification performance in this study. The proposed methods utilize One-hot encoding with VGG-16 for feature extraction to achieve 98% accuracy with BreakHis data while DBN for feature learning reaches 98% accuracy on BreakHis and 96% on Kaggle. SSGAN addresses unannotated images effectively with up to 89% accuracy. Through its application, deep learning technology proves to enhance breast cancer identification while decreasing the workload on medical pathologists. One-hot encoding remains efficient for computations yet the DBN extraction method produces superior features. The SSGAN model increases labeling accuracy when it uses available labeled data and unlabeled data to lower annotation expenses. Deep learning technologies validate their ability to transform breast cancer histopathological diagnosis through precision-enhanced efficient examination methods especially with semi-supervised GAN systems.

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Swati R. Nitnaware mail -

Bindu Madhavi Tummala mail -

Naga Siva Jyothi Kompalli mail -

Lakshmi Ramani Burra mail -

Nelli Sreevidya mail -

Gunavardini V. mail

link https://doi.org/10.54216/JISIoT.160222

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel Blockchain-Assisted Deep Learning Model for Enhancing Healthcare Data Security with Advanced Metaheuristic Optimization Techniques in Internet of Things

The Internet of Things (IoT) devices and technologies are more effective in the medical sector. It includes the combination of numerous interrelated sensor, systems, and devices for gathering, examining, and conveying health-related information for medicinal uses. In the healthcare field, Blockchain (BC) technology embraces huge latent for increasing the security and confidentiality of data. BC-aided intrusion detection on IoT healthcare methods is a new technique for increasing the privacy and security of complex health data. Patients have superior control throughout their information’s growth, granting or revoking access as needed, but healthcare employees will modernize data sharing and certify the reliability of significant data. On the other hand, deep learning (DL) is excellent for transforming healthcare analytics, presenting rapid and tremendously precise estimations of medicinal circumstances. This paper presents a Blockchain-Assisted Deep Learning Model for Enhancing Healthcare Data Security with Metaheuristic Optimization Techniques (BCDL-HDSMOT) model. The main intention of the BCDL-HDSMOT technique is to develop an effective method for enhancing data security in the medical sector. At first, the blockchain technique is applied in healthcare to enhance data security, interoperability, and transparency while ensuring patient privacy and efficient record management. Next, the data pre-processing stage employs min-max normalization to clean, transform, and organize input data into a suitable quality for analysis. Besides, the black widow optimization algorithm (BWOA) has been deployed for the feature selection process to select the relevant features from input data. For the classification process, the proposed BCDL-HDSMOT technique designs a versatile long-short-term memory (VLSTM) method. At last, the improved seagull optimization algorithm (ISOA)--based hyperparameter selection process is performed to optimize the classification results of the VLSTM method. The experimental evaluation of the BCDL-HDSMOT algorithm can be tested on a benchmark dataset. The wide-ranging outcomes highlight the significant solution of the BCDL-HDSMOT approach to the cyberattack detection process.

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R. Sugantha Lakshmi mail -

N. Suguna mail

link https://doi.org/10.54216/FPA.190229

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Automated Kidney Cancer Classification using White Shark Optimizer with Ensemble Majority Voting Model on Pathology Images

Kidney cancer is a lethal cancerous and very dangerous disease caused by genetic renal disease or by kidney tumors, and some patients might survive since there is no technique for earlier diagnosis of kidney tumor. Earlier diagnosis of kidney tumor assists physicians to begin proper treatment and therapy for the patient, which prevent kidney cancers and renal transplantation. Accurate classification of kidney tumor is vital for prediction and treatment planning. However, manual classification by pathologists could be subjective and time-consuming, and there can be considerable inter-observer variability. With the development of artificial intelligence (AI), automated tools enabled by machine learning (ML) and deep learning (DL) methods could predict cancers. This study designs a new white shark optimizer with an ensemble majority voting based kidney cancer classification (WSO-EMVKCC) technique on pathology images. The presented WSO-EMVKCC technique intends to identify the different grades of kidney cancer using DL and ensemble voting concepts. To accomplish this, the presented WSO-EMVKCC technique employs a deep convolutional neural network based Xception technique for the feature extraction process. Moreover, the WSO model has been used for the optimal hyperparameter tuning of the Xception approach. Furthermore, an ensemble majority voting classifier including three ML techniques like long short-term memory (LSTM), sparse autoencoder (SAE), and gated recurrent unit (GRU) models are employed for kidney cancer classification. The stimulation validation of the WSO-EMVKCC model is performed on the open access histology image database from Kaggle repository. The stimulated values illustrate the promising performance of the WSO-EMVKCC algorithm over other DL techniques.

groups

Ashrf Althbiti mail

link https://doi.org/10.54216/FPA.190230

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new