Terms Of Performance Metrics Research Articles

Evaluating Streamflow Forecasts in Hydro‐Dominated Power Systems—When and Why They Matter

AbstractThe value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large‐scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled‐water energy model that is subject to reservoir inflow forecasts with different levels of predictive performance. We implement the modeling framework on a real‐world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro‐dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.

A review of machine learning applications in Cacao Post-harvest management

Cacao beans play a crucial role in chocolate production, making them a highly relevant crop. The post-harvesting stage of cacao beans, encompassing classification, quality assessment, and fermentation, holds significant importance. With the increasing demand for marketable cacao beans, the need for reliable, accurate, and fast technologies has emerged. This study provides a comprehensive review of machine learning techniques in the post-harvesting stage of cacao beans from 2016 to the present. Analyzing 36 studies, it focuses on classification, quality assessment, and fermentation. The proposed framework includes the application domain, learning algorithms, performance metrics, and reported impacts. Notably, it explores various machine learning applications like classification, quality assessment, and fermentation, highlighting commonly used algorithms like ANN, CNN, and SVM. In terms of performance metrics, GLCM achieved the highest accuracy (99.61%) in cacao classification, ANFIS excelled in quality assessment (99.715%), and k-NN emerged as the most accurate for fermentation. This review serves as a valuable resource for researchers in the cacao bean sector, offering insights into machine learning advancements.

Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications

With the escalating demand for high-data-rate wireless services, visible light communication (VLC) technology has emerged as a promising complement to traditional radio frequency wireless networks. To further enhance the achievable rate and error performance in non-orthogonal multiple access-based VLC downlinks, an efficient power allocation scheme named enhanced gain difference power allocation (EGDPA) is proposed for a multiple-input multiple-output VLC system. The power factors are determined by considering users’ channel gains and utilizing the residual allocation principle, which focuses on the remaining power available after allocating it to the previous users. In addition, the impacts of the user distribution and transmission power are investigated, and the performance metrics in terms of achievable data rate, energy efficiency, and bit error rate are also analytically presented. Simulation results demonstrate that energy efficiency can be significantly improved and the achievable data rate gain can be enhanced by at least 6.25% with the proposed EGDPA scheme as compared with other traditional methods, confirming its superiority and validity for efficient multi-user accessing.

A knowledge-guided graph attention network for emotion-cause pair extraction

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.

A comparative study of lung disease classification using fine-tuned CXR and chest CT images

The diagnosis of lung disease is a challenging process that frequently combines clinical information, such as patient symptoms, medical history and test findings, with medical imaging, like X-rays or CT scans. The classification of lung diseases is very important in healthcare since it helps with diagnosis and treatment of many different lung diseases. A precise classification of lung conditions can aid doctors in choosing the best course of action and enhancing patient outcomes. Additionally, accurate classification can aid in evaluating the effectiveness of therapies, forecasting results and tracking the development of diseases. It is extremely important to accurately classify lung conditions. A comparison of a novel model for lung disease classification from chest CT and CXR images was presented in this paper. A modified VGG-16 model was used as the classification model. To improve the performance, a fine-tuning mechanism was added to the proposed model. The effectiveness of the suggested method is analyzed and compared on two distinct datasets in terms of performance metrics. The experimental outcomes showed that the suggested model performs better on the CXR image dataset.

A 0.5-V Four-Stage Amplifier Using Cross-Feedforward Positive Feedback Frequency Compensation

This paper presents a low-voltage CMOS four-stage amplifier operating in the subthreshold region. The first design technique includes the cross-feedforward positive feedback frequency compensation (CFPFC) for obtaining better bandwidth efficiency in a low-voltage multi-stage amplifier. The second design technique incorporates both the bulk-drain-driven input stage topology in conjunction with a low-voltage attenuator to permit operation at a low voltage, and improves the input common-mode range (ICMR). The proposed circuit is implemented using TSMC-40 nm process technology. It consumes 0.866 μW at a supply voltage of 0.5 V. With a capacitive load of 50 pF, this four-stage amplifier can achieve 84.59 dB in gain, 161.00 kHz in unity-gain bandwidth, 96 deg in phase margin, and 5.7 dB in gain margin whilst offering an input-referred noise of 213.63 nV/Hz @1 kHz, small-signal power-bandwidth FoMss of 9.31 (MHz∙pF/μW), and noise-power per bandwidth-based FoMnpb of 1.15 × 10−6 ((µV/Hz)·µW/Hz). Compared to the conventional bulk-driven input stage design technique, it offers improved multi-parameter performance metrics in terms of noise, power, and bandwidth at a compromising tradeoff on ICMR with respect to bulk-driven amplifier design. Compared with conventional gate-source input stage design, it offers improved ICMR. The amplifier is useful for low-voltage analog signal-processing applications.

MFOA-Bi-LSTM: An optimized bidirectional long short-term memory model for short-term traffic flow prediction

Within Intelligent Transportation Systems (ITSs), modeling of traffic flow assumes a pivotal role, as it is crucial for alleviating traffic congestion and reducing carbon emissions. Developing a reliable and sustainable model for predicting traffic flow is highly challenging due to the fluctuations and nonlinear characteristics of traffic flow. In addition to being a practical problem, accurately forecasting traffic flow provides substantial challenges to the researchers working in this area. The aim of this study is to develop a complex framework for forecasting short-term traffic flow with the purpose of improving prediction accuracy. In order to attain this goal, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with a Modified Firefly Optimization Algorithm (MFOA) known as MFOA-Bi-LSTM has been proposed. The MFOA optimization technique has been utilized to optimize the hyperparameters of the deep learning models. Moreover, the optimization of the Bi-LSTM network prediction model is achieved through the application of the MFOA technique, which is renowned for its ability to converge quickly, maintain robustness and perform extensive global searches. To better capture complex patterns in the input data and increase prediction accuracy, model’s architecture makes use of the bidirectional capabilities of Bi-LSTM layers. This combination of Bi-LSTM and FOA is proposed for optimizing complex sequence modeling tasks where selecting the right hyperparameters for the model is challenging and it allows for automated hyperparameter tuning and often leads to improved model performance. In terms of performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Correlation Coefficient (r), the proposed model significantly outperforms the other selected models and study findings confirm its appropriateness for the predictive task.

Detection Model of Substation Personnel Safety Protection Tools Based on Improved YOLOv5

Substation operation and maintenance personnel face the hazards of high voltage and strong current in their work. Therefore, it is crucial to accurately and promptly detect the condition of safety protection tools for these personnel. This paper proposes a detection model that enhances YOLOv5‘s substation personnel safety protection tool detection capabilities. The CBAM attention mechanism was integrated into the YOLOv5 model using this approach, while the feature fusion network PANet was replaced with the CB-BiFPN structure. Finally, DIoU was used instead of GIoU loss function to improve the convergence speed and detection accuracy of the model. Experimental results demonstrate that our improved YOLOv5 model achieves high accuracy and fast processing speed with a mean average precision (mAP) of 89.23% at 32 frames per second (FPS). Our proposed method outperforms other related object detection models such as Faster RCNN, SSD, and YOLOv5 in terms of performance metrics. This study improves safety protection tool detection accuracy and provides valuable insights for real-time safety management and control in substations.

Performance Analysis of Cooperative NOMA for Different Power Allocation Strategies

Cooperative None Orthogonal Multiple Access (C-NOMA) is a promising approach for 5G and beyond communication networks. Users will benefit from the full bandwidth of the channel without time constraints. NOMA’s features are incomplete without efficient power allocation that ensures power distribution among users fairly. Integrating power management (allocation) with Cooperative-NOMA (C-NOMA) may improve the system metrics. In addition, a physical layer security (PLS) is added to make the process of sending and receiving safe, and the system works in an integrated manner, preventing any interrupting or eavesdropping inside or outside the network. This paper demonstrates the user and channel performance of C-NOMA with Amplify-and-forward (AF) and Decode-and-forward (DF) approaches. The analysis is performed by varying the power allocation techniques to derive the best system configurations. The simulation results confirmed the analytic findings and showed that the proposed system outperforms orthogonal multiple access (OMA), conventional NOMA, and conventional cooperative NOMA, enhancing the performance metrics in terms of throughput, sum rate, and outage probability. The bit-error-rate (BER) of the far user can be identical to that of the near user if power allocation is properly set. All techniques excelled except for the fixed power allocation, which had the same BER. The Channel capacity and outage probability were also considered. A slight variation in the channel capacity in all the experiments for different numbers of users was found. The generalized power allocation for AF and DF models had the optimum channel capacity close to 14 bps/Hz. Moreover, far users always had a higher outage probability than near users and channels, and generalized power allocation was the highest outage probability technique when the transmit power was close to (4 dB).

Machine Learning based Intelligent System for Breast Cancer Prediction (MLISBCP)

Risks of death from Breast Cancer (BC) are drastically rising in recent years. The diagnosis of breast cancer is time-consuming due to the limited availability of diagnostic systems such as dynamic MRI, X-rays etc. Early detection and diagnosis of breast cancer significantly impacts life expectancy as current medical technologies are not advanced enough to treat patients in later stages effectively. Even though researchers have created many expert systems for early detection of BC such as WNBC, AR + NN system, AdaBoost ELM etc., but still most expert systems frequently lack adequate handling of the class imbalance problem, proper data pre-processing, and systematic feature selection. To overcome these limitations, this work proposes an expert system named “Machine Learning Based Intelligent System for Breast Cancer Prediction (MLISBCP)” for better prediction of breast cancer using machine learning analytics. The suggested system utilises the ‘K-Means SMOTE’ oversampling method to handle the class imbalance problem and ‘Boruta’ feature selection technique to select the most relevant features of the BC dataset. To understand the effectiveness of the proposed model – MLISBCP, its performance is compared with various single classifier based models, ensemble models and various models present in literature in terms of performance metrics- accuracy, precision, recall, F1-score and RoC AUC Score. The results reveal that the MLISBCP obtained the highest accuracy of 97.53 % with respect to existing models present in the literature.

Automatic classification of parasitized fruit fly pupae from X-ray images by convolutional neural networks

The parasitoid wasp Diachasmimorpha longicaudata (Hymenoptera: Braconidae) has been used for the biological control of fruit fly larvae in many countries. Due to the large scale of production, quality control systems must be utilized to assess insect quality to optimize prerelease activities. However, the parasitism rate, and the unparasitized puparia, which become waste pupae, can only be known after waiting for the emergence of the adults from sampled pupae (ca. 16 days at 25 °C). This waiting time can result in resources being wasted. A way to speed up the quality control of pupal batches and increase the accuracy of the production process would be to combine digital radiography of the parasitized pupae and deep learning methods for the automatic classification of images. Therefore, the purpose of this work was to discriminate parasitized from waste pupae through X-ray images and to test the suitability of 7 Convolutional Neural Networks to classify the pupal images. Radiographic images from 11-day-old pupae allowed the in vivo identification of immature stages of the parasitoid, preserving the viability of the wasp. A positive correlation was found between the number of emerged parasitoids and the ones identified in the X-ray images. In terms of performance metrics, the accuracies achieved by all CNN-based neural architectures were higher than 97%, indicating a high predictive classification power. At least three of the CNNs tested showed great potential as classifiers of X-ray images of parasitized pupae and can be indicated for practical applications in quality control programs.

Classification Algorithms for Liver Epidemic Identification

Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver. It is a crucial organ essential for the proper functioning of the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, it regulates the balance of hormones in the body filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With this clinicians can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique.

Comparative Study of Routing Protocols for IoT Networks

Abstract: The Internet of things (IoT) is widely used for communication between portable and intelligent heterogeneous devices, like laptops, smartphones, computers, etc. IoT networks are popular in the modern era because they allow data to be exchanged anywhere when connected to the internet. IoT networks have several challenges, including those related to routing, connectivity, privacy, security, and other issues. The major challenge is routing in terms of choosing the best route for sharing data in IoT networks; IoT routing algorithms use more time and energy. In this paper, we review relevent literature and patents and also various routing approaches are categorized into groups, like multicast, clustering, emergency application, traffic, location, tree, and residual energy based approach, and they are compared based on several parameters, like energy consumption, network lifetime, path length, packet delivery ratio, and network latency. In terms of performance metrics, like energy consumption, network lifetime, reliability, efficiency, and packet delivery ratio, the comparison shows that Routing Protocol for Low Power and Lossy Networks (RPL), Efficient Tree-based Self-organizing Protocol (ETSP), Collection Tree Protocol (CTP), and Fast Multi-constrained Multicast Routing Algorithms (FAMOUS) are the best protocols. The best approach is a tree-based one since it solves the larger problem in the hierarchy with the least amount of time complexity.

Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.

Transition metal nitrides embedded in N-doped porous graphitic Carbon: Applications as electrocatalytic sulfur host materials

While transition metal nitrides (TMNs) are promising electrocatalysts, their widespread use is challenged by the complex synthetic methodology and a limited understanding of the underlying electrocatalytic mechanisms. Herein, we describe a novel synthesis of TMNs (including Mo2N, NbN, and ZrN) and explore their potential as electrocatalysts to affect sulfur cathode reactions. The TMNs were prepared in-situ using a process that simultaneously infuses nitrogen-doped porous graphitic carbon (designated as TMN@N-PGC). The methodology avoids the use of ammonia, which poses safety risks due to its flammability and toxicity. Analysis of the d-p hybridized orbitals formed between the transition metal ions and sulfur species revealed that the antibonding orbitals are empty. Thus, TMNs with more negative d-band centers exhibit stronger affinities towards polysulfides. NbN facilitated polysulfide conversion as well as Li2S detachment, and thus featured a high electrocatalytic capability for promoting cathode kinetics. Lithium−sulfur (Li–S) batteries containing NbN@N-PGC exhibited the highest performance metrics in terms of specific capacity (1488 mA h g−1 at 0.1 C), rate capacity (521 mA h g−1 at 6 C), and cycling stability (603 mA h g−1 at 0.5 C after 1300 cycles, corresponding a capacity decay of 0.030% per cycle). Li−S cells with high sulfur loadings also exhibit outstanding performance.

Multi-objective optimization of mixed-model assembly lines incorporating musculoskeletal risks assessment using digital human modeling

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.

A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniques

Accurate time series forecasting is crucial in various fields, including finance, economics, healthcare, transportation, and energy. Recently, deep learning methods have gained significant attention in time series forecasting. Despite their ability to model complex and nonlinear data patterns, their performance may be degraded when there is not sufficient input data. To address this issue, in this study, we propose a methodology that performs feature-based representation through time series featurization and adds the relevant features as an auxiliary input to deep learning models. Specifically, after conducting the featurization step, as another contribution, we develop a novel combined feature selection algorithm called JAYA-Genetic (JaGen) and employ it as the feature selection technique. The JaGen algorithm takes advantage of both the JAYA and Genetic algorithms to make balance between exploring and exploitation processes. To assess the efficacy of the suggested methodology, two deep learning models, a convolutional network and a multi-head attention mechanism are used. According to the findings of experiments carried out on ten Covid-19 datasets, two product demand time series datasets, and two public time series datasets, the derived models outperform their regular counterparts and current models in terms of performance metrics like symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

Deep Autoencoder-Based Integrated Model for Anomaly Detection and Efficient Feature Extraction in IoT Networks

The intrusion detection system (IDS) is a promising technology for ensuring security against cyber-attacks in internet-of-things networks. In conventional IDS, anomaly detection and feature extraction are performed by two different models. In this paper, we propose a new integrated model based on deep autoencoder (AE) for anomaly detection and feature extraction. Firstly, AE is trained based on normal network traffic and used later to detect anomalies. Then, the trained AE model is employed again to extract useful low-dimensional features for anomalous data without the need for a feature extraction training stage, which is required by other methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). After that, the extracted features are used by a machine learning (ML) or deep learning (DL) classifier to determine the type of attack (multi-classification). The performance of the proposed unified approach was evaluated on real IoT datasets called N-BaIoT and MQTTset, which contain normal and malicious network traffics. The proposed AE was compared with other popular anomaly detection techniques such as one-class support vector machine (OC-SVM) and isolation forest (iForest), in terms of performance metrics (accuracy, precision, recall, and F1-score), and execution time. AE was found to identify attacks better than OC-SVM and iForest with fast detection time. The proposed feature extraction method aims to reduce the computation complexity while maintaining the performance metrics of the multi-classifier models as much as possible compared to their counterparts. We tested the model with different ML/DL classifiers such as decision tree, random forest, deep neural network (DNN), conventional neural network (CNN), and hybrid CNN with long short-term memory (LSTM). The experiment results showed the capability of the proposed model to simultaneously detect anomalous events and reduce the dimensionality of the data.

A novel hybrid model to predict concomitant diseases for Hashimoto’s thyroiditis

Hashimoto’s thyroiditis is an autoimmune disorder characterized by the destruction of thyroid cells through immune-mediated mechanisms involving cells and antibodies. The condition can trigger disturbances in metabolism, leading to the development of other autoimmune diseases, known as concomitant diseases. Multiple concomitant diseases may coexist in a single individual, making it challenging to diagnose and manage them effectively. This study aims to propose a novel hybrid algorithm that classifies concomitant diseases associated with Hashimoto’s thyroiditis based on sequences. The approach involves building distinct prediction models for each class and using the output of one model as input for the subsequent one, resulting in a dynamic decision-making process. Genes associated with concomitant diseases were collected alongside those related to Hashimoto’s thyroiditis, and their sequences were obtained from the NCBI site in fasta format. The hybrid algorithm was evaluated against common machine learning algorithms and their various combinations. The experimental results demonstrate that the proposed hybrid model outperforms existing classification methods in terms of performance metrics. The significance of this study lies in its two distinctive aspects. Firstly, it presents a new benchmarking dataset that has not been previously developed in this field, using diverse methods. Secondly, it proposes a more effective and efficient solution that accounts for the dynamic nature of the dataset. The hybrid approach holds promise in investigating the genetic heterogeneity of complex diseases such as Hashimoto’s thyroiditis and identifying new autoimmune disease genes. Additionally, the results of this study may aid in the development of genetic screening tools and laboratory experiments targeting Hashimoto’s thyroiditis genetic risk factors. New software, models, and techniques for computing, including systems biology, machine learning, and artificial intelligence, are used in our study.