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

Sonia Ayachi; Zaman Fahad

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

Fusion: Practice and Applications | 2026

Comparative Analysis of Fuzzy Time Series Methods for Predicting Indonesia's Export Performance

This study aims to forecast the export volumes of oil and gas and non-oil and gas sectors in Indonesia, as export volumes reflect the economic condition of a country. The research utilizes data from BPS, spanning from January 2018 to December 2023, and employs the Fuzzy Time Series (FTS) methodology. Six different methods are applied: First-Order FTS Chen, First-Order FTS Cheng, Second-Order FTS Chen, Second-Order FTS Cheng, Markov Chain FTS, and Time-Invariant FTS. FTS is a predictive technique based on fundamental logic and various concepts and rules within fuzzy sets. The prediction accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). The MAPE values for these six methods are compared to determine the most suitable method for this case study. The findings reveal that First-Order FTS Chen achieves an accuracy of 4.07%, First-Order FTS Cheng 4%, Second-Order FTS Chen 1.61%, Second-Order FTS Cheng 1.58%, Markov Chain 3.96%, and Time-Invariant 8.88%. The results indicate that Second-Order FTS Cheng provides the highest accuracy and is effective for predicting the export volumes of oil and gas and non-oil and gas sectors in Indonesia.    

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Lintang Patria mail -

Zahratul Amani Zakaria mail

link https://doi.org/10.54216/FPA.210103

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

A Systematic Review of Blockchain and Metaheuristic Algorithms for Secure and Scalable Healthcare Systems

The integration of blockchain technology and metaheuristic optimization has transformed healthcare systems by improving security, scalability, and data interoperability. Blockchain ensures decentralization, immutability, and privacy, making it a viable solution for electronic medical records (EMRs) and secure healthcare data management. Meanwhile, metaheuristic algorithms optimize blockchain networks by enhancing transaction efficiency, consensus mechanisms, and real-time medical data processing. This paper systematically reviews recent advancements in blockchain and metaheuristics for healthcare applications. We discuss existing privacy-preserving models, AI-driven optimization techniques, and hybrid consensus mechanisms, addressing their strengths and limitations. Through a structured methodology, we analyze research trends, security challenges, and computational bottlenecks. This study encompassed 300 research articles from nine global databases. Then, inclusion and exclusion criteria were applied, leading to the exclusion of 144 studies and the retention of 156 studies. Subsequently, quality assessments were conducted, resulting in the final inclusion of only 8 studies for data extraction. A three-phase methodology was followed: planning, conducting, and reporting. The studies covered the period from January 2020 to January 2025, and 10 evaluation questions were used to assess the quality of the studies. Our findings reveal that while blockchain enhances data security and interoperability, metaheuristic-driven AI further optimizes system efficiency. However, challenges such as scalability constraints, energy consumption, regulatory compliance, and AI-based cyber threats remain significant. Future research should focus on developing lightweight blockchain architectures, quantum- resistant cryptographic models, and federated AI-enhanced security frameworks to address these issues. By leveraging advanced blockchain and AI-driven metaheuristics, healthcare systems can achieve greater resilience, efficiency, and adaptive security.

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Karam Hatem Alkhater mail -

Mohana Shanmugam mail -

Pritheega Magalingam mail

link https://doi.org/10.54216/FPA.210105

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

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.  

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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.  

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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.  

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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.

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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