Novel Breast Cancer Prediction Using a Lazy-Learning Approach with Linear Neural-Network Search and Euclidean Distance

S. Amsavalli; Vetripriya M.; R. Sivasankari; Vetri Selvan M.; Vijayakumar K.

doi:https://doi.org/10.54216/FPA.210114

Fusion: Practice and Applications | 2026

A Novel Features Selection Method for Misuse Intrusion Detection System based on RNA Encoding and Raita Algorithm

The significance of the Intrusion Detection System (IDS) is due to its capability in detecting attacks over the network. The current paper proposes a new feature selection method for misuse intrusion detection systems based on RNA encoding, where the proposed method includes five steps. Firstly, the KDD-Cup99 dataset is used and then select random records are used for both training and testing. Secondly, RNA encoding to encode each possible value in the dataset into RNA characters. Thirdly, the keys and their locations are extracted by dividing the achieved RNA sequences from previous steps into blocks with different sizes, then finding the most repeated blocks, choosing them as keys, and storing their location. The next step is the proposed feature selection method based on the extracted keys and their locations, depending on the place of the key within the feature number. Finally, the Raita algorithm for matching to search for keys before and after the applied features selection method. In terms of IDS performance evaluation, experimental outcomes of the proposed feature selection method show the capability of optimizing the time complexity and metrics.  

groups

Dunia Alawi Jarwan mail -

Omar Fitian Rashid mail -

M. Jasim Mohammed mail -

Shaymaa E. Sarhan mail -

Hind Moutaz Al-Dabbas mail -

Maythem K. Abbas mail

link https://doi.org/10.54216/FPA.210106

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Hybrid AI-Based Approach for Early and Accurate Rice Disease Detection

Rice plant disease detection is crucial in agriculture to prevent crop loss and enhance productivity. Traditional manual inspection methods often lead to inaccuracies, delays in diagnosis, and excessive pesticide use. To address these challenges, this study proposes an Artificial Layered Fuzzy Neural Network-based African Vulture Optimization (ALFNN-AVO) algorithm for early and accurate detection of rice plant diseases. The proposed framework integrates multiple advanced techniques, including Cross Fusion former (CF former) for feature extraction, Squeeze Excitation (SE) fusion for enhancing feature representation, and Spatial Fuzzy C-Means (SPFCM) for precise segmentation of affected plant regions. Furthermore, an Artificial Layered Depth Separable Neural Network (ALDSNN) is employed for multi-class classification of rice plant diseases. The Differential Bitwise African Vultures Optimization Algorithm (DBAVOA) is introduced to optimize the hyperparameters, ensuring improved convergence and classification performance. Experimental results validate the efficiency of the proposed model, achieving an accuracy of 98.87% and an execution time of 0.09 minutes, outperforming existing methodologies. The findings demonstrate that the proposed framework offers a reliable and computationally efficient solution for real-time rice plant disease detection, contributing to sustainable agricultural practices.  

groups

Noorishta Hashmi mail -

Mohammad Haroon mail

link https://doi.org/10.54216/FPA.210107

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Enhancing EEG-Based Emotion Recognition in Computer Games Using KNN Optimized by the iHOW Optimization Algorithm

Emotion recognition using electroencephalogram (EEG) signals has become a pivotal area in affective computing, particularly within the context of human–computer interaction and game-based environments. This study aims to enhance the accuracy and robustness of EEG-based emotion classification by introducing a hybrid framework that combines the k-Nearest Neighbors (KNN) classifier with advanced metaheuristic feature selection techniques. Using the publicly available GAMEEMO dataset, which includes EEG recordings from 28 subjects engaged in four emotionally distinct computer games (boring, calm, horror, and funny), EEG data were acquired through a 14-channel Emotiv Epoc+ device and labeled using the Self-Assessment Manikin (SAM) scale. Baseline machine learning models including Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), and KNN were evaluated, with KNN achieving the highest base line performance. The KNN classifier was further optimized using several metaheuristic algorithms—namely WAO, BBO, GWO, GA, FA, PSO—and the proposed Improved Human Optimization Algorithm (iHOW). Experimental results show that the iHOW+KNN model achieved the best overall performance with an accuracy of 96.85%, sensitivity of 95.50%, specificity of 95.82%, and F1-score of 95.54%. Visual assessments using heatmaps, radar plots, and confidence intervals further validated the model’s reliability. These findings demonstrate the effectiveness of the iHOW+KNN framework in addressing the challenges of high-dimensional EEG data and highlight the potential of wearable EEG devices for real-time emotion recognition in affective computing applications into user experiences within the gaming environment.

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Abdelhameed Ibrahim mail -

Christos Gatzoulis mail -

El-Sayed M. El-kenawy mail -

Marwa M. Eid mail

link https://doi.org/10.54216/FPA.210108

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Bilingual LLM-Based Platform for Job Search and Career Guidance: NaukariCraft

Finding jobs in today’s world is similar to finding a needle in the haystack. The modern job-search platforms present a language barrier for native-speakers and inexperienced candidates, making it difficult for them to compete in the job search race. NaukariCraft, a bilingual (Hindi & English) job search platform makes it easy for users to look for jobs, gain industrial insights, save time by finding relevant jobs tailored to skills and resume, building ATS friendly resume, and ATS score analyzer. NaukariCraft provides full guidance to novel applicants helping them find direction towards jobs tailored to their resume. Using advanced technology like Large Language Model (LLMs) and agents, NaukariCraft enhances user experience, improves employability through resume analysis, and reduces application fatigue. This paper outlines the methodology, proposed work, result, conclusion and future development avenues for NaukariCraft.

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Shruti Sharma mail -

Satvik Bhardwaj mail -

Sonakshi Vij mail -

Gopal Chaudhary mail

link https://doi.org/10.54216/FPA.210111

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Enhancing Classification Accuracy through Cluster-Based Ensemble Learning and Adaptive Weighting

As digital devices continue to process ever-increasing volumes of complex data, ensuring accurate and efficient machine learning performance has become a significant challenge. Traditional ensemble learning methods often attempt to address these issues through data sampling or partitioning; however, such approaches can introduce biases and fail to fully capture the underlying structure of the data. To address these limitations, this paper proposes a novel classification framework that integrates clustering with adaptive weighting strategies. The process begins by dividing the training data into clusters, each representing a specific subset of the overall data distribution. Separate machine learning models are then trained on these clusters, allowing each model to specialize in different areas of the data. When analyzing a test instance, its relationship to the individual clusters is evaluated using two key measures: the correlation coefficient, which assesses feature similarity, and the Mahalanobis distance, which calculates the statistical proximity to the cluster center. These values are subsequently used to generate optimized weights that determine the influence each model should have in the final ensemble prediction. By aligning model contributions with the structural similarities between the test and training data, the proposed approach enhances both the reliability and precision of classification. Experimental results demonstrate that this cluster-aware ensemble consistently outperforms both baseline and advanced classifiers on benchmark datasets.

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Mustafa Radif mail -

Zainab Fahad alnaseri mail -

Salam saad alkafagi mail -

Ali Hakem Al-saeedi mail -

Riyadh Rahef Nuiaa Alogaili mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/FPA.210109

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Multiscale Feature Extraction for Remote Sensing Image Analysis Using Discrete Wavelet Transform

Remote sensing image evaluation faces continual challenges in extracting discriminative capabilities from complex; multi-scale landscapes the use of conventional spectral-spatial techniques, which often fail to capture hierarchical structures correctly. This examine proposes a brand-new methodology that leverages the discrete wavelet remodel (DWT) for multi-scale characteristic extraction. It is carried out thru Python and the PyWavelets library to offer an open-source, reproducible solution. The framework decomposes pictures into subscales of path and directional detail throughout multiple scales, extracting statistical and textural descriptors optimized for remote sensing obligations. A complete assessment of 500 multispectral patches (Sentinel-2, Landsat-8, and high-decision sensors) demonstrates advanced overall performance in land cover class, accomplishing an accuracy of 92.4%, outperforming uncooked pixel methods (84.1%), important issue evaluation (PCA) (87.3%), and GLCM-based totally techniques (89.6%). A sensitivity analysis famous that Daubeches wavelet 4 at decomposition level three improves function discriminability, in particular for agricultural textures (91.2% accuracy) and concrete limitations (IoU=0.873), while directional subbands (LH/HL) reduce transition area mistakes by way of 23%. The computational efficiency (184 ms/megapixel) remains possible. These consequences show that DWT is an effective and handy device for improving faraway sensing analysis, with the full code and datasets being made publicly available to promote community adoption and foster innovation.

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Mohammed Abdulhasan Hussein mail -

Rajaa Daami Resen mail -

Ali Nafea Yousif mail -

Oday Ali Hassen mail -

Ansam A. Abdulhussein mail

link https://doi.org/10.54216/FPA.210110

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Image Tag Generation Based on Deep Features Using Deep Learning Techniques

The task of automatically generating descriptive and accurate image tags has gained significant attention in recent years due to the exponential growth of image data. Traditional methods for image tagging rely on manual annotation, which is time-consuming and subjective. Automated imagine description fills the gap between visual content and human comprehension, making it vital for activities such as information retrieval, editing, and accessibility. The expanding number of unannotated photographs makes manual tagging impossible. This paper provides a deep learning-based system that combines CNNs for feature extraction, RNNs for caption production, and attention techniques to focus on significant image areas. The model uses a sequence-to-sequence architecture to create coherent captions using pre-trained CNN features and attention-enhanced RNNs. Experiments on datasets such as Flickr8k and Flickr30k show higher performance, as evidenced by BLEU, ROUGE, and CIDEr measures. This approach provides a scalable, cutting-edge solution for image captioning, with potential applications in video analysis, enriched language production, and larger datasets.  

groups

Heba Adnan Raheem mail -

Hiba Jabbar Aleqabie mail -

Ameer Sameer Hamood Mohammed Ali mail

link https://doi.org/10.54216/FPA.210112

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Quantifying the Impact of AI Integration in Software Development: An Empirical Analysis of Efficiency, Ethics, and Organizational Readiness

This study empirically examines how artificial intelligence (AI) is changing the online software development ecosystem. Data from 30 types of software professionals in various roles is used to examine opportunities, challenges and ethical considerations, trends in AI-enhanced software development as well technological innovation research methods. Major findings show substantial increases in efficiency of development processes (39.3% decrease in development time) and the quality of the codes (53.3% less flaws/KLOC). However, organizations also face major challenges. For instance, there is a significant skill gap to bridge (severity rating 4.2/5) and expensive implementation costs to put into practice. This study provides a fact-based guide for organizations interested in integrating AI technologies into their software development procedures. The paper also outlines practical inputs that must be made by software practitioners.

groups

Sonia Ayachi Ghannouchi mail -

Zaman Fahad Badday mail

link https://doi.org/10.54216/JISIoT.180101

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Secure and Decentralized Plant Disease Detection via Federated Learning with Differential Privacy and Homomorphic Encryption

Plant disease detection using deep learning has achieved high accuracy, but traditional centralized training poses significant privacy risks and incurs high data transmission costs. This study presents a privacy-preserving federated learning (FL) framework for plant disease diagnosis that enables decentralized model training across geographically distributed agricultural sites. Rather than transferring raw farm data to a central server, local models are trained on edge devices and share only model updates. To address data heterogeneity from diverse climates, soils, and plant species, we introduce adaptive aggregation strategies that improve model generalization. Furthermore, we incorporate differential privacy and homomorphic encryption to ensure secure model updates and protect sensitive information from potential breaches. Experimental evaluations on benchmark datasets, including Plant Village and real-world field images, show that the proposed FL-based system achieves comparable accuracy to centralized models while significantly enhancing data privacy and reducing communication overhead. The framework maintains over 93% classification accuracy across 38 plant disease categories, with minimal degradation from added privacy mechanisms. Additionally, we analyze the trade-off between accuracy and communication efficiency, demonstrating the method’s practicality in bandwidth-constrained rural environments. The proposed system offers a scalable, secure, and field-deployable solution for real-time plant disease monitoring, supporting the widespread adoption of AI in precision agriculture without compromising data confidentiality.

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Vetripriya M. mail -

S. Amsavalli mail -

R. Sivasankari mail -

Vetri Selvan M. mail -

N. Kanimozhi mail

link https://doi.org/10.54216/FPA.210113

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Novel Breast Cancer Prediction Using a Lazy-Learning Approach with Linear Neural-Network Search and Euclidean Distance

Breast cancer is the most prevalent cancer-affecting women worldwide and remains a major cause of mortality. Early detection and accurate prognosis are critical to improving survival outcomes. This study introduces a novel predictive model for breast cancer diagnosis that integrates a lazy learning paradigm with the K-Nearest Neighbors (KNN) algorithm, optimized through a Linear Nearest Neighbor (NN) Search technique and the use of Euclidean distance as the similarity measure. The dataset, comprising 4,024 patient records with 15 clinical and demographic attributes, was obtained from a public repository and underwent rigorous preprocessing, including handling of missing values, normalization, and categorical encoding. The classification model was trained and evaluated using 1:9 cross-validation, with K values ranging from 1 to 9 and a constant batch size of 100 to identify the optimal configuration. Among various configurations tested, the model with K=5 demonstrated the highest performance, achieving an accuracy of 88.02%, precision of 0.87, and recall of 0.88. Additional performance metrics such as F-measure, Matthews Correlation Coefficient (MCC), and Kappa statistic further confirmed the robustness of the selected configuration. The proposed model shows superior predictive capability compared to traditional settings and can serve as a decision-support tool for clinicians. The findings suggest that the combination of lazy learning, effective neighbor search strategy, and robust distance metric can substantially enhance the predictive accuracy of breast cancer diagnosis. This study highlights the potential of machine learning-based tools in clinical oncology, offering a data-driven approach for early intervention and patient outcome improvement.

groups

S. Amsavalli mail -

Vetripriya M. mail -

R. Sivasankari mail -

Vetri Selvan M. mail -

Vijayakumar K. mail

link https://doi.org/10.54216/FPA.210114

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new