AI-based System for Transforming Text and Sound to Educational Videos

M. E.; S. M.; M.  El. R.

doi:https://doi.org/10.54216/FPA.210115

Journal of Intelligent Systems and Internet of Things | 2026

An Adaptive Mutation-Aware Test Case Ordering Framework Using Deep Learning and Quantum-Behaved Multi-Objective PSO

  In regression testing, rapidly identifying defects is crucial for maintaining software quality amid frequent code changes. Traditional test case ordering methods, despite extensive research, often overlook the subtle but important relationship between test executions and mutations introduced during code modifications. This paper presents an adaptive mutation-aware test case ordering framework that integrates predictive modeling with swarm-based multi-objective optimization to address this gap. The approach begins by transforming test cases into enriched feature vectors, incorporating mutation coverage, historical performance, execution cost, and statement-level weighting. A supervised deep learning model is employed to predict the likelihood of each test case uncovering seeded defects. These predictions are subsequently fed into a Quantum-Behaved Particle Swarm Optimization (QPSO) engine, which generates an optimal execution sequence by jointly optimizing fault detection, execution cost, reuse potential, and coverage diversity. The proposed framework is demonstrated using a simple Java program and rigorously validated on real-world projects from the De-fects4J benchmark. Experimental results consistently show improvements in APFD, mutation scores, and execution efficiency, confirming the feasibility and scalability of the proposed system.

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S. Sowmyadevi mail -

Anna Alphy mail

link https://doi.org/10.54216/JISIoT.180114

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Satellite Imaging Based Risk Management in Cloud IoT Network Using Machine Learning Techniques

The consistent improvement of remote sensing (RS) technology has resulted in an easy access to a large volume of satellite imagery. There is a need for effective and scalable solutions for widening the application of RS in different fields and making it work efficiently in practical situations. This research propose novel technique in satellite image gathering and cloud IoT network risk management using machine-learning model. Here the cloud IoT network has been used in satellite image collection and this network security analysis has been carried out using secure trust based cryptographic blockchain model. Then this collected image has been classified using convolutional bayes fuzzy markov perceptron basis function model. Experimental analysis has been carried out in terms of accuracy, QoS, recall, latency, scalability. Proposed model attained accuracy of 97%, QoS of 94%, LATENCY of 96%, Scalability of 95%, RECALL of 93%. These results assist decision-makers, planners, and scientists studying remote sensing select an appropriate image classification system for tracking a dynamic, fragmented, and varied landscape.

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Jyotsnarani Tripathy mail -

T. Krishna Murthy mail -

S. Manjula mail -

Sukanya Ledalla mail -

Alla Rajendra mail -

P. Lakshmi Harika mail -

K Boopathy mail

link https://doi.org/10.54216/JISIoT.180115

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Diverse Geographical Region Analysis Based on Deforestation Rate Using Remote Sensing Image and Machine Learning Techniques

With direct implications for the regional climate, biogeochemistry, hydrology, and biodiversity, land cover change has been identified as one of the top priorities for the development of sustainable management plans. Among the primary causes of global warming are deforestation and forest fragmentation, which have profound effects on biodiversity preservation and ecosystem functioning. Machine learning techniques, like those employed in computer vision, have become widely used, making it possible to segment satellite images semantically to distinguish between areas that are forested and those that are not. This study presents a novel method for segmenting and classifying UAV images to detect deforestation using machine-learning models. In this case, noise reduction as well as normalisation is applied to input, which consists of UAV-based forest region photos. Semantic U-convolutional regressive neural network combined with deep radial quantile temporal neural network was then used to segment and classify this image. The suggested model's simulation analysis is assessed based on several metrics, including F-1 score, normalized coefficient ratio, average precision, AUC, and detection accuracy. proposed method yielded 97% detection  accuracy, 93% normalized coefficient ratio, 91% AUC, F-1 score of 94% and 95% AVERAGE PRECISION.

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Abhilash S. Nath mail -

Manu Gupta mail -

J. Sirisha Devi mail -

A Babisha mail -

D. Venkata Ravi Kumar mail -

B. Rama Subba Reddy mail

link https://doi.org/10.54216/JISIoT.180116

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Edge Cloud IoT Model Based Marine Life Analysis Using Machine Learning Algorithms

The amount of marine data is such that it is pointless, and at times infeasible, to attempt training deep learning models on personal workstations. In this work, we present the advantages of cloud based distributed learning in training of deep learning (DL) model and management of big data. Moreover, large volumes of marine big data are classically through wire networks, which are costly, if at all deployable, to maintain. This research propose novel technique in marine life analysis based on remote sensing image using edge cloud IoT model and machine learning algorithms. Here the edge cloud IoT model has been used for collecting remote sensing image in marine life analysis. This remote sensing image has been processed for noise removal as well as normalization. Then this image is feature extracted as well as classified utilizing principal Gaussian convolutional fuzzy encoder with Bayesian reinforcement Markova algorithm. Experimental analysis has been carried out in terms of classification accuracy, average precision, recall, F1 score, AUC for various marine life dataset. proposed technique obtained 97% Classification   accuracy, 95% Average precision, 93% Recall, 88% AUC, 94% F1 SCORE.

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Gagan Kumar Koduru mail -

S. Kalaimagal mail -

M. Srilakshmi Preethi mail -

G. L. Narasamba Vanguri mail -

Shivanadhuni Spandana mail -

M. Syed Rabiya mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180117

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Cloud IoT with Remote Sensing Data Segmentation and Classification Using Deep Learning Model for Sustainable Agriculture

  Sustainable Development Goals of United Nations are focused on enhancing agricultural production that has the potential to be transformational at the local as well as the global level. The available technologies in agriculture management that are based on Internet of Things (IoT) encourage sustainable production of more food by farmers, which contributes significantly to the achievement of these SDGs. The aim of this research is to propose novel technique in sustainable agriculture field analysis based on cloud IoT model with remote sensing and deep learning model. Here the cloud IoT model is used in agriculture field based remote sensing data analysis. This image has been segmented using watershed K-means temporal neural network (WKMTNN) and classification is carried out using deep quantile regressive Boltzmann machine (DQRBM). The experimental analysis has been carried out in terms of random accuracy, average precision, sensitivity, specificity for various agriculture field dataset. Proposed model attained average precision 96%, sensitivity 93%, random   accuracy 98%, and Specificity 95%.  These results highlight the superiority of the moisture estimation framework against their regression-based counterparts.

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T. Shanmugapriya mail -

RM. Rani mail -

Gaddam Ravindra Babu mail -

T. Srinivasulu mail -

S. Saranya mail -

S. Gopinath mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180118

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Climate Change Prediction in Urban Environment Using UAV Imaging Based on Cloud IoT and Deep Learning Techniques

Advancements in Unmanned Aerial Vehicles (UAVs), popularly identified as drones, offer unprecedented opportunities to improve various applications of Extensive Internet of Things (IoT). In this framework, Deep Learning (DL) techniques are considered a practical alternative for improving the real-time obstacle detection and avoidance performance of fully autonomous UAVs. This research propose novel technique in urban environment climate change detection utilizing UAV image based on cloud IoT with deep learning model. Here the UAV images has been collected through cloud IoT module and prepared for dataset. This dataset with UAV images has been processed for filtering and contour reduction by normalization. Then processed image features are extracted utilizing graph cut fuzzy convolutional ResNet attention neural network with moath firefly sparrow colony optimization model. The simulation results has been analyzed for various UAV dataset in terms of training accuracy, average precision, recall, QoS, scalability. Proposed technique Average precision of 97%, QOS of 92%, SCALABILITY of 96%, training accuracy of 98%, RECALL of 95%.

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M. Prema Kumar mail -

P. Chinnasamy mail -

B. Bala Abirami mail -

Juvvala Sailaja mail -

S. Bhuvana mail -

Sai Krishna Vunnam mail

link https://doi.org/10.54216/JISIoT.180119

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Design and Construction of the Word Embedding Model for Automated Bug Detection Using Deep Learning Techniques

Software quality assurance teams can increase productivity and efficiency by expediting the issue-fixing process through automatic localization of bug files. Although source code and bug reports provide valuable semantic information, current bug localization techniques typically underuse it. Numerous deep learning and word embedding models have been developed over time. The word-embedding model used to represent bug reports and the deep learning model used for categorization determine how effective those methods are. Aim of this research is to construct word-embedding method, which has been automated for bug detection using deep learning techniques. Here the input data has been collected as software design based monitored data and processed. Then this data has been analyzed using Bi-LSTM voting vector word embedding model and the feature classification is carried out using convolutional naïve bays attention perceptron neural network in bug detection model. The experimental analysis is carried out in terms of training accuracy, precision, Mean square error, F-1 score, and recall. Furthermore, cross-training datasets from the same and distinct domains are used to gauge how effective the suggested approach is. For datasets in the same domain, suggested system obtains a good high accuracy rate; for datasets in separate domains, it achieves a poor accuracy rate.

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Khasimbee Shaik mail -

K .V. Satyanarayana mail -

Tirimula Rao Benala mail

link https://doi.org/10.54216/JISIoT.180120

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Feature Weight-Based Optimization in Software Development Model Using Meta Heuristic Machine-Learning Algorithms

System users are increasingly interested in software correctness and efficiency checks prior to usage. Programmers in the twenty-first century are therefore making a conscious effort to create software that is more accurate, more efficient, and less prone to bugs. A software development model utilizing metaheuristic machine learning algorithms involves using metaheuristic optimization techniques to enhance various aspects of the software development lifecycle, such as optimizing machine learning models, hyperparameters, and even software architecture. This research propose novel technique in feature weight model based optimization in software development utilizing Meta heuristic ML method. Here the feature weight and feature selection is carried out for software model using support additive regression Laplacian score perceptron neural network. Then the software model parameter optimization is carried out using ant binary swarm component encoder optimization method. Simulation analysis is carried out in terms of training accuracy, MAR (Mean absolute residual), Mean balanced relative error (MBRE), F-measure.

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N. Durga Devi mail -

Tirimula Rao Benala mail

link https://doi.org/10.54216/JISIoT.180121

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Machine Learning Model Based Urban Temperature Analysis with Fuzzy Reinforcement Neural Network

  Temperature increases in metropolitan areas are referred to as urban heat island (UHI) effect. In recent decades, urbanization as well as dramatic increase in population of cities have exacerbated the impact of UHI. The uneven development and growth of the metropolis will lead to an uneven rate of temperature growth in the corresponding area. This work proposes a new machine learning approach based on temperature pattern analysis to determine the rate of deforestation, representing the diversity of geographical regions. The proposed model collect temperature pattern based deforestation data as well as processed for noise removal and normalization. Then this data features has been extracted as well as classified utilizing kernel principal fuzzy reinforcement NN with variational Gaussian encoder markov model. Experimental analysis is carried out in terms of random accuracy, mean precision, AUC, normalized co-efficient, F1 score. Proposed method mean precision was 94%, normalized co-efficient was 97%, AUC was 95%, random accuracy 98%, F1-score 93%.  The most important land use categories causing LST increases were determined by analyzing the landscape composition at the class level.

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L. Pallavi mail -

Gattu Shravani mail -

J. Sirisha Devi mail -

Bandaru Satya Lakshmi mail -

M. Pushpalatha mail -

S. Gopinath mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180122

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

AI-based System for Transforming Text and Sound to Educational Videos

Technological developments have produced methods that can generate educational videos from input text or sound. Recently, the use of deep learning techniques for image and video generation has been widely explored, particularly in education. However, generating video content from conditional inputs such as text or speech remains a challenging area. In this paper, we introduce a novel method to the educational structure, Generative Adversarial Network (GAN), which develop frame-for-frame frameworks and are able to create full educational videos. The proposed system is structured into three main phases in the first phase; the input (either text or speech) is transcribed using speech recognition. In the second phase, key terms are extracted and relevant images are generated using advanced models such as CLIP and diffusion models to enhance visual quality and semantic alignment. In the final phase, the generated images are synthesized into a video format, integrated with either pre-recorded or synthesized sound, resulting in a fully interactive educational video. The proposed system is compared with other systems such as TGAN, MoCoGAN, and TGANS-C, achieving a Fréchet Inception Distance (FID) score of 28.75%, which indicates improved visual quality and better over existing methods.

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M. E. ElAlami mail -

S. M. Khater mail -

M. El. R. Rehan mail

link https://doi.org/10.54216/FPA.210115

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new