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Fusion: Practice and Applications | 2025

Enhancing Adverse Drug Reaction Classification of Attention Deficit Hyperactivity Disorder Diagnosis Data Using Deep Learning with Optimization Algorithm

Adverse Drug Reaction (ADR) is a significant global public health issue and the main cause of death. Generally, the effects of ADR are complex. Clinically, they can cause major patient damage and, in some cases, death. Besides, this outcome in significant healthcare costs financially owing to enlarged hospital visits, extra treatments, and harm to productivity. Therefore, early recognition and mitigation of ADRs are vital for the patients. Enhancing the early detection of ADRs and deadliness could severely reduce the harm to patients, improve patient safety, decrease healthcare costs, and increase the efficacy of the drug development procedure. Conventional pre-clinical toxicity tests are expensive, time-consuming, and frequently fail to forecast human-specific toxic effects. Artificial Intelligence (AI)-based deep learning (DL) has been quickly adopted in numerous areas, with healthcare, for its latent to manage huge datasets, find out patterns, and generate predictions. This study presents a new Adverse Drug Reaction Detection through Deep Learning and Improved Red-Tailed Hawk Algorithm (ADRD-DLIRTHA). The main intention of the ADRD-DLIRTHA model is to enhance the detection and classification process of ADR using advanced hybrid and optimization techniques. At first, the data normalization stage applies z-score normalization for converting input data into a beneficial set-up. Furthermore, the proposed ADRD-DLIRTHA method designs a convolutional neural network and long short-term memory (CNN-LSTM) technique for the classification process. At last, the improved red-tailed hawk (IRTH) algorithm-based hyperparameter selection process has been applied to optimize the classification results of the CNN-LSTM system. A wide range of experimentation was led to authorize the performance of the ADRD-DLIRTHA system. The simulation results specified that the ADRD-DLIRTHA model emphasized advancement over other existing techniques

groups
N. Deepaletchumi mail -
R. Mala mail
link https://doi.org/10.54216/FPA.190104

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2025

Gated Recurrent Fusion in Long Short-Term Memory Fusion

Fusion techniques on enhancing the efficiency of Long Short-Term Memory (LSTM) networks are dominating across a variety of domains. To handle sequential data while integrating from various sources is often challenging using LSTM techniques. Fusion methods that integrate different models enhances LSTM’ ability to handle complex correlations in the data. This paper examines early, late and hybrid fusion techniques. The study provides fusion approaches to enhance LSTM networks to efficiently handle complex multimodal data across self-navigating models. The findings reveal that the hybrid fusion techniques outperform traditional methods in terms of accuracy and generalization of various tasks. This paper proposes the Gated Recurrent Fusion (GRF) approach to demonstrate its performance to handle multimodal and temporal models in a supervised recurrence. The findings report 10% enhancement in terms of precision rate

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Anita Venugopal mail -
Aditi Sharma mail -
Preetish Kakkar mail -
Daya Nand mail -
Arvind R. Yadav mail -
Gaurav Kumar Ameta mail
link https://doi.org/10.54216/FPA.190105

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2025

Optimizing Diabetes Diagnosis: HFM with Tree-Structured Parzen Estimator for Enhanced Predictive Performance and Interpretability

This study proposes the novel machine learning concepts to enhance both prediction accuracy of diabetes detection and interpretation of diagnostic models. First, the methodology uses multiple imputations by chained equations (MICE) to complete data before analysis through missing data imputation procedures. The class imbalance problem is solved through the implementation of Synthetic Minority Over-sampling Technique (SMOTE). The Interquartile Range (IQR) outlier detection method helps remove outliers because it enhances model robustness. The hybrid RFE-WWO selection process combines Recursive Feature Elimination (RFE) with Water Wave optimization (WWO) to select important features that strike the right balance between model complexity and prediction accuracy. The HFM framework contains the Hybrid Fusion Model as its essential component, which merges AdaBoost's and CatBoost's most favorable aspects. The hyperparameter optimization with TPE leads to model tuning which reaches a prediction accuracy of 97.84% through the application of Tree-Structured Parzen Estimator. The entire approach delivers enhanced accuracy and it improves precision along with recall metrics and F1 score performance of the predictive model. The framework shows significant potential for early diagnosis by merging these advanced techniques since ensemble methods are essential for healthcare data analysis while accurate interpretable models are vital to create dependable diagnostic tools.

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Hemalatha Dendukuri mail -
Kachapuram Basava Raju mail -
S. Phani Praveen mail -
Janjhyam V. Naga Ramesh mail -
Vahiduddin Shariff mail -
N. S. Koti Mani Kumar Tirumanadham mail
link https://doi.org/10.54216/FPA.190106

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2025

Multiple Feature-Based Recurrent Neural Network for Highly Accurate Ransomware Detection in Android Devices

Ransomware or crypto-ransomware is a big headache to digital media and transactions nowadays. Generally, Ransomware affects the operating system and transfers the valuable information and data stored in the system. Some ransomware attacks the system and corrupts the system file, making it useless to the user. Data encryption with a private key is also one of the attaching fashions of some types of ransomwares. Most ransomware attacks are reported in android operating system-based devices. The solution to ransomware is only the earlier identification of an attacked pattern in the operating system and removal of it. Artificial Intelligence (AI) plays a major role in various kinds of attack detection and classification processes. Machine learning (ML) technique can be used to train and classify the presence of ransomware in android-based devices. Various parameters, such as the characteristics of applications' permission access to various inputs of the devices. The data can be used to train the Recurrent Neural Network (RNN), the most popular and highly accurate ML module that performs a highly accurate classification process. The performance can be evaluated using various sensitivity evaluation metrics such as accuracy, sensitivity, specificity, and precision.

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Vyom Kulshreshtha mail -
Deepak Motwani mail -
Pankaj Sharma mail
link https://doi.org/10.54216/FPA.190107

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Precision Driven Human Recognition Model for Adaptive Information Retrieval in Learning Environments

Face recognition technology plays a vital role in modern educational systems by enabling efficient and accurate student identification. The growing demand for efficient and accurate student identification systems has highlighted the limitations of conventional face recognition methods, particularly in handling variations in pose, lighting, and occlusions. To address this, our Precision-Optimized Human Recognition Model builds an Adaptive Information Retrieval System utilizing a Histogram of Oriented Gradients (HOG)-based detector for face detection and a ResNet-34-based Deep Metric Learning Model for face recognition. The system encodes facial features and performs identity verification using Euclidean distance for precise and reliable student identification. By integrating these techniques, the model ensures real-time data retrieval with high accuracy and adaptability to diverse conditions. The proposed approach enhances computational efficiency while maintaining robust recognition performance, making it a scalable and practical solution for identity verification in educational institutions.

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S. Hemamalini mail -
J. Beryl Sharon mail -
M. Dharshini mail -
M. Indu mail -
SK Mithun mail -
C. Sathish Kumar mail
link https://doi.org/10.54216/JISIoT.160102

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2025

A Computationally Efficient Topologized Graphical Method for Neutrosophic Transportation Optimization: Cost Minimization, Performance Metrics, and Python Implementation

Pentagonal Neutrosophic Set is a powerful technique for modelling situation in real life where there is uncertainty, indeterminacy, and inconsistency, the PNTP is an advanced version of classical transportation problems. Traditional transportation models do not perform well with imprecise data unlike PNTP that offers a powerful framework that can handle truth, indeterminacy, falsity, non-membership, and membership parameters resulting in a more realistic decision about logistics. In this work, we present a novel Topologized Graphical Method (TGM) for resolving the PNTP, which uses graphical notations to visualize and analyse intricate interactions in transport networks under neutrosophic circumstances. In this paper, an efficient and structured solution methodology has been developed for optimization of PNTP, with TGM incorporated to provide a systematic approach to the PNTP while significantly reducing computational burden. To improve the pragmatism of the method, an algorithm is established in Python to convert the neutrosophic transportation model into a classical transportation problem, which contributes to computing efficiency and helps the decision-makers get the optimal solutions with little efforts. Solutions to numerical examples and case studies, which show that our method achieves better performance than conventional approach in minimizing transportation cost, optimizing resources allocation, and reducing the burden of calculation, provide validation of the proposed method. This research employs Pentagonal Neutrosophic Sets with the TGM as well as the use of the Python programming language to offer an effective and accurate decision-support instrument, improving transportation planning in uncertain dynamic environments. In addition, the findings provide tangible insights into how PNTP could be beneficial in real-world applications, particularly in fields like logistics, SCM, and network design, where managing uncertain information is essential. The next step of this work will be analysing the integration of AI and ML techniques with the presented method to gain improvements on predictive analytics, automation, and real-time decision-making abilities in transportation problems.

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Charles Robert Kenneth mail -
R. C. Thivyarathi mail -
E. Kungumaraj mail -
K. Sridharan mail -
V. Dhanasekaran mail -
K. A. Venkatesan mail
link https://doi.org/10.54216/IJNS.260310

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. However, it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and TRA models contribute to accelerating the robustness and acc of pedestrian detection systems. Therefore, this research models a novel marine predator algorithm with DL-based pedestrian detection and classification (MPADLB-PDC) method. The objective of the MPADLB-PDC system lies in the accurate recognition and identification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLB-PDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural networks (DFFNNs), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC technique was authorized on the pedestrian database, and the outcome can be studied in terms of various aspects. The experimentation values validated the better outcome of the MPADLB-PDC approach compared to other approaches.

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Hima Bindu Gogineni mail -
Hemanta Kumar Bhuyan mail -
E. Laxmi Lydia mail
link https://doi.org/10.54216/JISIoT.160103

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Feature Selection and Stability Analysis using Ensemble Techniques

Selecting the most relevant feature subset for a task is demanded and recommended for high accuracy and reduced model training time. Ensemble learning has shown superior results in classification; hence, we propose an ensemble method for feature selection and shown stability analysis for the selected feature set. The research question being investigated is whether ensemble methods are effective at selecting informative features in a dataset and if the selected features are stable compared to other feature selection methods. This paper presented a tree-based ensemble learning approach for feature selection. Our approach for ensemble feature selection includes function perturbation with the voting ensemble, an ensemble with a fixed number of features, and an ensemble with a contiguous number of features. Ensemble learning is found to be superior to other traditional feature selection algorithms. Ensemble learning algorithms are implemented on two high-dimensional microarray biomedical datasets. From our experimental study, it is observed that the voting ensemble outperforms other ensemble techniques, thereby reducing feature subset size and achieving higher accuracy. Stability analysis of all the algorithms has been studied and it is found that all ensemble techniques have higher stability than the traditional feature selection methods. Thus, ensemble learning proves to be a superior technique for feature selection. Our results demonstrate that the proposed method is effective in identifying relevant features and stable features and can improve the performance of machine learning models.

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Dipti Theng mail -
K. K. Bhoyar mail -
Prashant Pawade mail
link https://doi.org/10.54216/JISIoT.160104

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things |

Integrating IoT and smart AI for Enhanced Sustainability in freight forwarding companies Performance

The following study investigates the role and impact of IoT and Al technologies on operational efficiency, sustainability, and cost optimization of freight forwarding companies. Their goals are to measure the effects of these technologies on logistics performance, assess sustainability improvements like decreased carbon emissions and waste, and identify cost-saving drivers for AI and IoT integration. H1: The operational efficiency of IoT and AI should enhance information sharing, route planning, and warehouse management significantly H2 claims that it will contribute to the reduction of carbon emissions and waste production by allowing real-time tracking, optimizing the usage of materials throughout the production cycle. H3- Cost Reduction in Logistics Operations through AI-based Automation, Predictive analytics and Improved Asset Management The approach was a quantitative research design, and data were obtained from 240 respondents from five large freight forwarders (companies): DHL Global Forwarding; Kuehne + Nagel; DB Schenker; XPO Logistics; and CEVA Logistics. Objective: Improvements after adoption are analyzed using structured questionnaires to measure key performance indicators (KPI) and frequency analysis and percentage calculation methods. The results confirm the transformative role of IoT and AI in freight logistics, increasing operational efficiency, sustainability, and cost efficiency. Logistics performance must be further optimized through continued investment in digital innovation.

groups
Apeksha Garg mail -
Sudha Vemaraju mail
link https://doi.org/10.54216/JISIoT.160105

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

An Enhancement of YOLOV3-Tiny Model for Turmeric Plant Disease Detection

Turmeric is a rhizomatous crop recognized for its medicinal effects which requires significant observation to ensure appropriate growth and progression. Turmeric plant diseases cause yield losses impacting food production systems and causing economic losses. Early prevention of these diseases is crucial for improving agricultural productivity. For this reason, The Improved YOLOV3-Tiny Model (IY3TM) was developed using Cycle-GAN and Convolutional Neural Network (CNN) with residual network for the early turmeric plant disease detection. However, this model leads to the omission of vital details along with the exact positioning of key attributes, thereby decreasing prediction accuracy. To resolve this, Convolutional and Vision Transformer model for Turmeric Diseases Detection (ConViT-TDD) is proposed for the prediction of turmeric plant diseases. ConViT-TDD is integrated into IY3TM with a self-attention mechanism and CNN-based global perspective to enhance the performance of the model A ConViT-TDD block involves the input channel transformation, the channel as well as spatial attention mechanism and global-minded transformers. The input channel transformation utilizes a convolutional layer to minimize the dimension of input channel and reduces the computational complexity. Global-minded transformers generate a feature vector based on the input channel transformation that is then transmitted to the encoder component. By collecting channel weights and spatial weights, respectively, the channel and spatial attention modules enhance the model's sensitivity to certain channel attributes and spatial locations, hence altering the feature representation of those channels and spatial locations. The attention module can adaptively change the weights of channel and spatial features for improved feature extraction and fusion. Once the initial attributes are reformed, the IY3TM detects and classifies the turmeric plant diseases. The test outcomes reveal that the ConViT-TDD model accomplishes an overall accuracy of 93.16% on the collected turmeric plant diseases images which is contrasted with the classical CNN models.

groups
Shylaja Santhosh mail -
Revathi Thiyagarajan mail
link https://doi.org/10.54216/JISIoT.160106

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new