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

Y. Sreeraman; D. Jagadeesan; J. Jegan; T. Vivekanandan; A. Srinivasan; G. Asha

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

Financial Technology and Innovation | 2023

WASPAS Multi-Criteria Decision-Making Method for Assessment Effectiveness and Performance Intelligent Transportation Systems Alternatives

The assessment of Intelligent Transportation Systems (ITS) plays a vital role in understanding their effectiveness, efficiency, and impact on transportation networks. This abstract provides an overview of the criteria for assessing ITS and highlights the importance of a comprehensive and multidimensional approach. The requirements discussed include safety, efficiency, mobility, environmental impact, user satisfaction, cost-effectiveness, scalability and interoperability, data security and privacy, technological reliability and resilience, regulatory and policy compliance, equity and accessibility, system integration, innovation and future-readiness, stakeholder engagement, performance monitoring and evaluation, resilience and disaster preparedness, social and economic impact, and continuous improvement and adaptation. By considering these criteria, stakeholders can gain valuable insights into the performance and benefits of ITS, aiding in decision-making, policy development, and future planning for transportation systems. This study uses multi-criteria decision-making (MCDM) methodologies, such as the assessing attractiveness method and the weighted aggregated sum product assessment (WASPAS) method. The WASPAS method is used to rank the alternatives. We used 18 criteria and 8 alternatives to be organised. The sensitivity analysis is conducted to check the stability of the results.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/FinTech-I.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique

This research is about the increasing cybersecurity challenges posed by modern malware threats and argues for an improved approach through optimized machine learning algorithms. We apply a Tree-structured Parzen Estimator (TPE) for hyperparameter tuning, focusing on the optimization of tree-based models such as Random Forest and Gradient Boosting. Our methodology includes careful correlation analysis, variable distribution examination, and feature importance assessment to make our models more robust and transparent. We present comprehensive visualizations that demonstrate the results of our optimized approach, which show improved accuracy, precision, and recall in malware detection. Our findings highlight the significance of feature engineering and model tuning, revealing subtle patterns indicative of malicious behavior. The findings indicate that our model provides a method that not only improves detection capabilities but also emphasizes the need for continuous improvement and innovation in addressing the ever-changing nature of malware threats.

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Ahmed Aziz mail -

Sanjar Mirzaliev mail -

Yuldashev Maqsudjon mail

link https://doi.org/10.54216/IJAACI.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Enhancing Information Fusion from UAV-Captured High-Altitude Infrared Imagery through Machine Learning

Unmanned aerial vehicles (UAVs) equipped with high-altitude infrared imaging have revolutionized data collection, providing better spatial and temperature resolutions. However, an effective way to fuse and interpret this multidimensional data remains a challenge. Therefore, this research tackles this issue by incorporating machine learning specifically the YOLO object detector to fuse and analyze information from UAV-captured high-altitude infrared images. The process entails a careful fusion of data, feature extraction, and model configuration that is tailored to the unique qualities of infrared imagery. Furthermore, the confabulated YOLO model performs exceptionally well in detecting and localizing objects within the thermal spectrum. Results showed precise identification of objects as well as their localization thus indicating potential for advanced aerial surveillance and monitoring. This research represents a significant advancement in situation awareness across environmental monitoring, infrastructure inspection, and disaster response among other areas hence demonstrating the transformative ability of machine learning in aerial imaging analysis.

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Mustafa El-Taie mail -

Aaras Y. Kraidi mail

link https://doi.org/10.54216/IJAACI.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2024

Securing the Skies: A Study of Cybersecurity Measures in Unmanned Aerial Vehicles

This study examines the importance of cybersecurity in Unmanned Aerial Vehicles (UAVs) due to the increasing technological advancements and subsequent vulnerabilities in these aerial systems. The rapid integration of UAVs across various sectors has led to a pervasive threat of cyber attacks, which necessitates comprehensive defenses to mitigate potential risks. This research outlines the complex landscape of UAV cybersecurity challenges through an in-depth analysis of attack scenarios and data features within the ECU-IoFT dataset. Using the XGBoost algorithm’s robustness, this study presents a proactive approach to classifying and mitigating cyber threats targeting UAV systems. Our findings demonstrate that XGBoost is effective at identifying different attack vectors, making it a possible key defense mechanism. The insights from this study not only highlight the changing nature of UAV cybersecurity but also provide practical steps for strengthening these aerial systems against imminent cyber threats to ensure their safe and resilient operation across multiple domains.

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Ahmed Mohamed Zaki mail -

Abdelaziz A. Abdelhamid mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/IJWAC.080106

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques.

groups

T. Vivekanandan mail -

J. Jegan mail -

D. Jagadeesan mail -

Y. Sreeraman mail -

N. Ch. S. N. Iyengar mail -

E. Purushotham mail

link https://doi.org/10.54216/JISIoT.110206

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Fusion of Brain Imaging Data with Artificial Intelligence to detect Autism Spectrum Disorder

Autism, a developmental and neurological disorder, impacts communication, interaction, and behavior, setting individuals with it apart from those without. This spectrum disorder affects various aspects of an individual's life, including social, cognitive, emotional, and physical health. Early detection and intervention are crucial for symptom reduction and facilitating learning and development. Recent advancements in machine learning and deep learning have facilitated the diagnosis of Autism by analyzing brain signals. This current study introduces an approach for Autism detection utilizing functional Magnetic Resonance Imaging (fMRI) data. The Autism Brain Imaging Data Exchange (ABIDE) dataset serves as the foundation, employing hierarchical graph pooling to abstract brain images into a graph structure. Graph Convolutional Networks are then used to learn node embeddings derived from sparse feature vectors. The model attains an accuracy of 87% on the 10-fold cross-validation dataset. This study proves to be cost-effective and efficient in identifying Autism through fMRI, making it suitable for near real-time applications.

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Monalin Pal mail -

Rubini P. mail

link https://doi.org/10.54216/FPA.140207

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

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.

groups

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

Details open_in_new

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.

groups

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

Details open_in_new

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

Details open_in_new

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.

groups

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