Journal of Intelligent Systems and Internet of Things

Genetic disorder is an outcome of transformation in deoxyribonucleic acid (DNA) system, which is progressed or natural from blood relation. Such transformations might lead to deadly illnesses like Alzheimer’s, cancer, and much more. The disorder of single gene kind is affected by a change in a solitary gene in DNA. The chromosomal disorder kind is affected when a genetic material or a portion of chromosome is removed or substituted in the structure of DNA. Complex illnesses are caused by the alteration in over one gene exhibit in the DNA. In recent times, the usage of artificial intelligence (AI)-based deep learning (DL) systems has exposed excellent achievement in the prognosis and prediction of diverse illnesses. The latent of DL models are employed to forecast genetic disorder at an initial phase utilizing the genome data for appropriate treatment. This paper presents a Deep Feature Selection Framework for Genetic Disorder Detection Using Convolutional Autoencoder and Metaheuristic Optimization (DFSFGDD-CAEMO) model. The aim of DFSFGDD-CAEMO model is to develop an accurate DNA-based genetic disorder classification model using advanced techniques for early and reliable disease diagnosis. Initially, the min-max normalization method is employed in the data pre-processing stage for converting an input data into a beneficial format. Besides, the Aquila optimizer (AO) method has been deployed for the selection of feature process in order to select the most significant features from a dataset. For the classification procedure, the proposed DFSFGDD-CAEMO technique designs Convolutional Autoencoder (CAE) method. At last, the hyperactive parameter tuning process is performed through enhanced pelican optimization algorithm (EPOA) for improving the classification performance of CAE model. The experimental evaluation of the DFSFGDD-CAEMO technique occurs using benchmark dataset. The experimentation results indicated out the enhanced performance of the DFSFGDD-CAEMO system when equated to existing approaches.

Wireless sensor networks used in disaster-struck areas experience the problem of energy constraints, which may negatively affect the data communication process. A novel energy-aware UAV relaying scheme is presented that incorporates SWIPT (Simultaneous Wireless Information and Power Transfer) to power the UAVs and their ground sensor devices. Dynamic power and flight path allocation according to the environmental conditions is achieved with dynamic reinforcement learning and, in particular, with a Proximal Policy Optimization (PPO) method. The system maximizes energy gathering at the sensor nodes and lengthens UAV flight life, and preserves high-quality signal transmission. The findings indicate a 23.5 dB increase in the SINR, 83.2 percent efficiency of energy harvesting, and an average of 43.2 minutes of endurance for the UAV. The success rate on the relay was 94.6 per cent, and a convergence of 12.3 seconds. The model also took the lead over other past ways in terms of mission coverage and energy efficiency in various simulation cases. This system enhances the resilience of disaster communication by effectively utilizing energy resources. Finally, it makes adaptation in real time and continued work in high-danger situations possible.

Mammals are susceptible to the lethal disease called coronavirus. This virus often infects humans through the aerial precipitation of any fluid released from the bodily part of the affected entity. This viral variant is deadlier than other sudden viruses. Given the ongoing thread which COVID-19 on health systems in the worldwide, there is a rising interest in development a mechanism that effective in terms of cost and classification. A mechanism for categorizing and scrutinizing the estimations derived from this virus' symptoms is proposed in this paper. The precision of various machine-learning classifiers is calculated in this study in order to determine the optimal classifier for COVID-19 identification. Because the COVID-19 dataset has the greatest precision of 100%, it was classified using AdaBoost and Bagging. Additionally, precision, recall, and F-score measures together with the ROC were deployed for evaluating detection performance to ensure the approach is capable and successful.

Artificial immune systems (AIS) represent an emerging facet of artificial intelligence, offering innovative solutions to a spectrum of problems. It draws inspiration from the biological immune system's fascinating properties, mechanisms, and principles, resulting in mathematical and computer-based implementations. In this paper, we aim to assess the accuracy of artificial immune systems as classification tools in the realm of Arabic handwriting recognition. Among the repertoire of immune-computing models, we focus on the Artificial Immune Recognition System (AIRS), Immunos, Clonal Selection Algorithm (CLONALG), and Clonal Selection Classification Algorithm (CSCA), which have garnered significant attention for their prowess in pattern recognition applications. To conduct this investigation, we leverage the comprehensive IFN-INIT Arabic handwriting database, which comprises contributions from 411 distinct writers. Feature selection plays a pivotal role in enhancing classification performance, and for this purpose, we harness the grey level co-occurrence matrix. In pursuit of a thorough comparative analysis, we also employ well-established classifiers such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Naive Bayes. The obtained results exhibit the promising potential of AIS-based classifiers in the context of Arabic handwriting recognition, offering insights into the evolving landscape of AI solutions in this domain.

This study investigates the impact of Digital Transformation (DT), Artificial Intelligence (AI), Internet of Things (IoT), Employee Collaboration (EC), and Communication on Organizational Citizenship Behavior (OCB) across different work models—Hybrid, Remote, and In-Office. A structured questionnaire was developed and administered to employees in the IT industry in Hyderabad to collect data. The major findings indicate that Digital Transformation, AI, IoT, and Employee Collaboration significantly enhance OCB across various work models. Conversely, Communication alone does not significantly affect OCB within different work settings. The integration of advanced digital tools, AI, IoT, and collaborative technologies is crucial for fostering positive employee behaviors, which are less achievable through communication alone. The study underscores the importance of leveraging digital transformation, AI, and IoT to optimize organizational outcomes, particularly when implementing diverse work models.

The transition from 5G to 6G wireless communication systems introduces new challenges, including scalability, privacy, and security. DLT (Distributed Ledger Technology) technology, with its decentralized and secure framework, offers a promising solution to address these issues in a 6G context. In a 6G environment, DLT can facilitate decentralized management, secure authentication, and trusted data exchanges. By leveraging DLT's distributed ledger system, it can support device identity verification, spectrum allocation, and secure data sharing across nodes, creating a trustworthy communication ecosystem. DLT and 6G integration enables efficient spectrum management, where smart contracts automate resource allocation, reducing bottlenecks and improving resource efficiency. Moreover, the decentralized nature of DLT enhances privacy and security by providing an authentication mechanism that works without central authority. This is crucial, as 6G will involve a vast number of connected devices. This research aims to explore the role of DLT in improving the security and scalability of 6G networks, investigate spectrum management techniques, and evaluate decentralized device authentication and trust mechanisms. Additionally, challenges such as latency, scalability, and DLT integration in 6G are examined. DLT's decentralized nature aids in network security and robustness, mitigating vulnerabilities by distributing control across nodes. It also streamlines resource allocation and device authentication, improving privacy. DLT enables users to manage access rights through decentralized mechanisms, fostering trust and compliance with privacy regulations. However, issues like latency due to transaction validation and the need for advanced techniques like sharding are challenges that must be addressed to optimize DLT for 6G applications.

  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.

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.

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.

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%.

  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.

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.

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.

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.

  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.