A Framework for Leveraging the Incorporation of AI, BIM, and IoT to Achieve Smart Sustainable Cities

Fawaz; Ashraf; Abdul Salam; Peter

doi:https://doi.org/10.54216/JISIoT.110207

Fusion: Practice and Applications | 2024

Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents

Digital document helps as the lifeblood of present communication, yet their vulnerability to tampering poses major safety anxieties. Digital text watermarking is an effective mechanism to protect the reliability of text-based data in the digital. Introducing a hidden layer of accountability and safety, allows individuals and organizations to trust the written word and make sure the truth behind all the files. Watermarking model identifies the tampering attack by inspecting the embedded signature for distortions or alterations. Watermarks can able to mechanically classify and repair themselves once tampered with, improving document resilience. Watermarking acts as a powerful tool to detect tampering attacks in digital document. By embedding strong and imperceptible watermarks in document distribution or creation, alterations are recognized by specialized procedure. This study introduces an Evolutionary Optimizer-powered Watermarking for Tampering Attack Detection in Digital Document (EO-WTAD3) model. The main intention of EO-WTAD3 approach is to support textual integrity using the applications of metaheuristic optimizer algorithm based watermarking technique for detecting tampering attacks in digital document. In the EO-WTAD3 method, a digital watermarking method has been proposed for the ownership verification and document copyright protection using data mining concept. Moreover, the EO-WTAD3 technique utilizes the concepts of data mining to define appropriate characteristics from the document for embedding watermarks. Moreover, fractional gorilla troops optimization (FGTO) algorithm can be applied for the assortment of optimal situation of watermarks in the content, ensuring both imperceptibility and strong to tamper. The performance validation of the EO-WTAD3 methodology takes place employing multiple datasets. The extensive result analysis portrayed that the EO-WTAD3 system accomplishes improve solution with other existing approaches with respect distinct aspects.

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Roman Shkilev mail -

Alevtina Kormiltseva mail -

Marina Achaeva mail -

Aiziryak Tarasova mail -

Marguba Matquliyeva mail

link https://doi.org/10.54216/FPA.140208

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG)

Depression is one of the common psychological disorders that affects many people all over the world. The primary typical behavior of depression is persistent low mood, and it is one of the main reasons for disability worldwide. Due to the lack of awareness, treatment, and social stigma, it is leading to suicide and self-harm. It is necessary to identify the depression at a very initial stage to overcome further complications that may lead to suicide. In recent years, certain studies have been done on identifying depression through Machine Learning and Deep Learning techniques. Electroencephalogram (EEG) can be used to detect depression since it is easy to record and non-invasive. The current paper focuses on developing an algorithm that will use the brain signals received through EEG and predict the person as Healthy or with Major Depressive Disorder (MDD) with the help of CNN through an asymmetry matrix, which achieved an accuracy of 89.5%, and it outperformed the previous traditional models. The current study shows that depression detection through EEG is one of the efficient techniques for detecting depression at its early stages.

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Madhu Sudhan H. V. mail -

S. Saravana Kumar mail

link https://doi.org/10.54216/FPA.140209

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Explainable Artificial Intelligence and Natural Language Processing for Unraveling Deceptive Contents

Deceptive content recognition in social media employing artificial intelligence (AI) includes the use of sophisticated techniques and machine learning (ML) methods to recognize deceptive or wrong data shared on numerous platforms. AI methods analyse textual as well as multimedia content, investigative patterns, linguistic cues, and contextual info to flag latent cases of deception. As a result of the use of natural language processing (NLP) and computer vision (CV), these systems identify subtle nuances, misrepresentation strategies, and anomalies in user-generated content. This active technique permits social media platforms, organizations, and consumers to recognize and diminish the spread of deceptive content, donates to a more reliable online atmosphere, and aids in fighting tasks modelled by misinformation and false news. This study offers a novel sine cosine algorithm with deep learning-based deceptive content detection on social media (SCADL-DCDSM) technique. The SCADL-DCDSM technique incorporates the ensemble learning process with a hyperparameter tuning strategy for classifying the sentiments. Primarily, the SCADL-DCDSM technique pre-processes the input data to change the input data into a valuable format. Moreover, the SCADL-DCDSM algorithm follows the BERT model for the word embedding process. Moreover, the SCADL-DCDSM technique involves an ensemble of three models for sentiment classification such as long short-term memory (LSTM), extreme learning machine (ELM), and attention-based recurrent neural network (ARNN). Finally, SCA can be executed for better hyperparameter choice of the DL models. The SCADL-DCDSM system integrates the explainable artificial intelligence (XAI) system LIME has been employed for a comprehensive explainability and understanding of the black-box process, enhancing correct deceptive content recognition. The simulation result analysis of the SCADL-DCDSM algorithm has been examined on a benchmark database. The simulation outcome illustrated that the SCADL-DCDSM methodology achieves optimum solution than other approaches in terms of different measures.

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Nadezda Pospelova mail -

Aiziryak Tarasova mail -

Natalya Subbotina mail -

Natalya Koroleva mail -

Nilufar Raimova mail -

E. Laxmi Lydia mail

link https://doi.org/10.54216/FPA.140212

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Enhancing Anomaly Detection in Pedestrian Walkways using Improved Sparrow Search Algorithm with Parallel Features Fusion Model

Anomaly detection in pedestrian walkways is a vital research area, widely employed to enhance the safety of the pedestrians. Because of the widespread usage of the video surveillance systems and the increasing number of captured videos, the conventional manual examination of labeling abnormal events is a laborious process. Therefore, an automatic surveillance system to accurately detect anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. This article develops an Improved Meta-heuristic with Parallel Features Fusion Model for Anomaly Detection in Pedestrian Walkways (IMPFF-ADPW) method. The main aim of the IMPFF-ADPW approach is to recognize the existence of anomalies in pedestrian walkways. To obtain this, the IMPFF-ADPW method applies a joint bilateral filter (JBF) for the process of noise removal. Besides, a parallel fusion process comprising NasNet Mobile and Darknet-53 models can be utilized for feature extraction. For the anomaly detection method, the deep autoencoder (DAE) model is applied and its hyperparameters are finetuned by using an improved sparrow search algorithm (ISSA). A wide of experimental outcomes can be applied to the UCSD database to illustrate the betterment of the IMPFF-ADPW methodology. The simulation values indicated the enhanced performance of the IMPFF-ADPW method over other existing techniques.

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Y. Sreeraman mail -

D. Jagadeesan mail -

J. Jegan mail -

T. Vivekanandan mail -

A. Srinivasan mail -

G. Asha mail

link https://doi.org/10.54216/FPA.140210

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Evolutionary Algorithm with Deep Learning based Fall Detection on Internet of Things Environment

Falling is among the most threatening event proficient by the ageing population. There is a necessity for the development of the fall detection (FD) system with the increasing ageing population. FD in an Internet of Things (IoT) platform has developed as a vital application with the rapidly increasing population of aging population and the essential for continuous health monitoring. Falls among the ageing can performance in serious injuries, decreased independence, and longer recovery periods. The FD approach can constructed on deep learning (DL) approaches, especially, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are capable in learning difficult patterns from the sensor data. The CNNs investigate the spatial features, but the RNNs approach the temporal dependencies, allowing accurate recognition of fall events. This study presents an Evolutionary Algorithm with Deep Learning based Fall Detection and Classification (EADL-FDC) methodology in the IoT platform. The projected EADL-FDC algorithm allows the DL approaches for the effective recognition and classification of falls for disabled and ageing people. In the presented EADL-FDC technique, the span-partial structure, and attention (SPA-Net) model is utilized for feature extraction purposes. In addition, the symbiotic organism search (SOS) approach was used for the parameter selection of the SPA-Net system. The deep belief network (DBN) model is applied to classify the fall events. Lastly, the moth flame optimization (MFO) algorithm can be utilized to finetune the hyperparameters related to the DBN algorithm. The stimulation analysis of the EADL-FDC method takes place on the fall detection dataset. The experimental outcome depicts the remarkable solution of the EADL-FDC technique over other existing DL methods.

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Elvir Akhmetshin mail -

Alexander Nemtsev mail -

Rustem Shichiyakh mail -

Denis Shakhov mail -

Inna Dedkova mail

link https://doi.org/10.54216/FPA.140211

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction

Intelligent data analytics for customer churn prediction (CCP) harnesses predictive modelling algorithms, machine learning (ML) techniques, and advanced big data analytics and also uncovers the underlying drivers and patterns of churn and detects customers at risk of churning. This business strategy help organization to implement retention efforts to decrease customer attrition and proactively detect at-risk customers. CCP allows businesses to take proactive measures such as targeted marketing campaigns, personalized offers, or enhanced customer service, to maintain valuable customer and decrease revenue loss. It is widely used in industries like telecommunications, subscription services, e-commerce, and finance to optimize customer retention strategies and enhance long-term profitability. ML algorithm can detect indicator and underlying trends that precedes churn by analyzing historical customer data, including transactional patterns, behaviors, demographics, and customer interaction. The study introduces Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning (IDA-HGOAML) Model for Customer Churn Prediction. The main intention of IDA-HGOAML method focuses on the prediction and classification of customer churns and non-churns. To do so, the IDA-HGOAML technique initially undergoes data pre-processing using Z-score normalization. The IDA-HGOAML model makes use of equilibrium optimization algorithm (EOA) for the feature selection (FS). Besides, the churn prediction method is implemented by the convolutional autoencoder (CAE) model. Finally, the HGOA is exploited for the optimal hyperparameter selection of CAE model, thereby enhancing the prediction results. A widespread experimental analysis were performed to validate the enhanced efficiency of the IDA-HGOAML method. The extensive outcomes indicated the improved prediction results of the IDA-HGOAML method over existing techniques in terms of different measures.

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Elvir Akhmetshin mail -

Nurulla Fayzullaev mail -

Elena Klochko mail -

Denis Shakhov mail -

Valentina Lobanova mail

link https://doi.org/10.54216/FPA.140213

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries

Intelligent System for Customer Churn Prediction (CCP) relates to a system or application that controls advanced artificial intelligence (AI), data analysis, and machine learning (ML) methods for anticipating and predicting customer churn in business or service. CCP approach utilizes various data sources comprising customer behavior and historical data, to create predictive method able of categorizing customers who are potential to leave or stop their engagement. By employing intelligent method, this system supports businesses in proactively addressing customer retention and executing manners to decrease churn, ultimately enhancing revenue retention and customer satisfaction. It connects wide data sources, comprising customer behavior and historical information, to progress difficult methods that can identify customers at risk of leaving or discontinuing their service or subscription. By leveraging deep learning (DL) method, this intelligent system enhances the efficiency and accuracy of customer churn prediction, allowing businesses to take proactive measures to maintain customers, maintain revenue, and develop customer satisfaction. This article presents an Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning (ISCCP-DTODL) methodology in Telecom Industries. The purpose of the ISCCP-DTODL system focuses on the design of intelligent systems for the effective prediction of customer churners and non-churners. To accomplish this, the ISCCP-DTODL system performs Z-score data normalization to preprocess the data. For feature selection and to reduce high dimensionality of features, the ISCCP-DTODL technique uses DTO algorithm. Besides, the ISCCP-DTODL technique makes use of hybrid CNN-BiLSTM model for churn prediction. At last, jellyfish optimization (JFO) based hyperparameter tuning approach can be employed to pick hyperparameters connected to CNN-BiLSTM technique. To display enhanced performance of ISCCP-DTODL technique, a widespread set of simulations was performed. The extensive results stated that ISCCP-DTODL model illustrates improved results than its current techniques in terms of dissimilar measures.

groups

Sergey Bakhvalov mail -

Eduard Osadchy mail -

Irina Bogdanova mail -

Rustem Shichiyakh mail -

E. Laxmi Lydia mail

link https://doi.org/10.54216/FPA.140214

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society

The financial crises has emphasized the part of financial relationship as a potential source of macroeconomic variability and systemic risk worldwide. Predicting financial crises using deep learning (DL) infers leveraging neural network (NN) to identify patterns indicative of future financial crisis and analyse complicated financial data. DL approaches such as recurrent neural network (RNN) or long short-term memory (LSTM) that process a massive quantity of past financial data such as geopolitical events, economic indicators, and market prices. These models target to identify refined connections and signals that can lead to an economic recession by learning from earlier crisis and their precursors. The problem resides in the complex and dynamic nature of financial market, demanding continuous training and modification of methods to retain significance in the aspect of developing financial condition. Although DL shows the potential to increase prediction capabilities, it's vital to accept the inherent ambiguity in financial market and the requirement for cutting-edge development of models to enhance their accuracy and reliability. This study proposes a jellyfish search algorithm based feature selection with optimum deep learning algorithm (JSAFS-ODL) for financial crisis prediction (FCP). The objective of JSAFS-ODL technique is classified the presence of financial crises or non-financial crises. To accomplish this, the JSAFS-ODL technique applies JSA based feature selection (JSA-FS) to choose an optimum set of features. Besides, RNN-GRU model can be used for the FCP. For enhancing the detection results of the RNN-GRU approach, chimp optimization algorithm (COA) can be utilized for the optimal tuning of the hyperparameters correlated to the RNN-GRU model. To guarantee the better performance of the JSAFS-ODL procedure, a series of tests were involved. The obtained values highlighted that the JSAFS-ODL technique reaches significant performance of the JSAFS-ODL technique.

groups

Eduard Osadchy mail -

Ilyоs Abdullayev mail -

Sergey Bakhvalov mail -

Elena Klochko mail -

Asiyat Tagibova mail

link https://doi.org/10.54216/FPA.140215

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Teaching risk assessment index system using neutrosophic AHP: Data Fusion method

The technology behind data fusion and picture instruction is continuously advancing along with the progression of society, and new applications for these skills are increasingly becoming available in everyday life to accommodate the expansion of scientific and technological knowledge. The term "data fusion technology" relates to a computer processing method that allows the use of a computer to automatically analyze and synthesize several observation data gleaned in time series in accordance with criteria to complete the necessary decision-making and evaluation tasks. But teaching surrounding multiple risks. This paper aims to identify and assess risks in teaching. The assessment risks in teaching are a critical task and contain multiple conflict criteria. We use Multi-Criteria Decision Making (MCDM). In this paper, we use an Analytical Hierarchy Process (AHP) to rank and compute each criterion's weights. We use five main and twenty sub-criteria. These criteria were evaluated under a neutrosophic environment—an example provided to present the outcomes of the proposed model. 

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Gustavo Alvarez Gómez mail -

Corona Gómez Armijos mail -

Ariel Romero Fernández mail -

Asmaa Ahmed mail

link https://doi.org/10.54216/FPA.140216

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

A Framework for Leveraging the Incorporation of AI, BIM, and IoT to Achieve Smart Sustainable Cities

This study investigates the significance of leveraging the incorporation of Artificial Intelligence (AI), Building Information Modeling (BIM), and the Internet of Things (IoT) to Achieve smart sustainable cities. Understanding their applications for Architecture, Engineering, and Construction (AEC) projects. The study encompasses three key dimensions: Design Optimization and Performance Simulation, Material and Life Cycle Sustainability, and Operational Efficiency and Environmental Impact. By leveraging BIM and AI, the research explores the integration of renewable energy, sustainable material selection, and smart building controls. BIM and AI experts were given a structured questionnaire, which was then analysed using SPSS. Descriptive and correlation analyses reveal significant positive correlations between energy efficiency and design visualization, construction sustainability visualization, as well as adaptability and education through visualization. The proposed framework deepens the capabilities of the combination of different technologies towards Smart Sustainable Cities. This work not only contributes theoretical insights to the field but also provides practical implications for industry professionals striving to enhance sustainable practices in AEC projects. Further studies to encourage a combination of other recent technologies to improve smart sustainable cities' performance.

groups

Fawaz Saleh mail -

Ashraf Elhendawi mail -

Abdul Salam Darwish mail -

Peter Farrell mail

link https://doi.org/10.54216/JISIoT.110207

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new