Genetic algorithm based production knowledge base for mechanical fault detection model

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms

Lung cancer is the deadliest kind of cancer, with the greatest death rate. After prostate cancer in males and breast cancer in women, lung cancer is the second most frequent type of cancer. According to forecasts from the International Association of Cancer Society (IACS), About 226,650 new cases of lung cancer (110,680 in men and 115,970 in women) and about 124,730 deaths from lung cancer (64,190 in men and 60,540 in women) in 2025. Early diagnosis of lung cancer and especially determining the stage of the disease (early, intermediate, or advanced) help determining the appropriate treatment plan for each patient.In this research, machine learning methods (Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM)), classification methods (K-mean clustering), and Genetic Algorithms (GA)are used to diagnose lung cancer. Data about lung cancer patients were collected. (1000 patients). Data pre-processing was performed, then the feature selection step was started using genetic algorithms. After feature selection, the genetic algorithm and machine learning tools are used to diagnose lung cancer. The dataset was randomly divided into 80% for training and 20% for testing. The genetic algorithm and decision tree achieve the best results, with 100% accuracy, then KNN with 99% accuracy, SVM with 98% accuracy, and the k-mean cluster has less accuracy at 67%.

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data . Method : Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors. Modeling was done in Python software with K -Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms . Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion : With less classification error compared to similar studies, the PSO -SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers.

Classification model for blast furnace status based on multi-source information

ABSTRACTObjectiveIn spinal surgery, precise identification of high‐speed bur milling states is crucial for patient safety. This study investigates whether integrating tactile and auditory perception can enhance the accuracy of milling state detection in robot‐assisted cervical laminectomy.MethodsBased on the mathematical and physical model of vibration and sound in high‐speed bur milling bone, the feasibility of employing vibration and sound characteristics to identify the milling states of high‐speed bur is studied systematically. Cervical laminectomy was performed on the cervical spine of the sheep. During the signal acquisition process, acceleration sensors and microphones were installed to collect vibration and sound signals, respectively. Seven milling states were set up in the experiment: (1) Milling depths of cortical bone (CTB): 0.5, 1.0, and 1.5 mm; (2) Milling depths of milling of cancellous bone (CCB): 0.5, 1.0, and 1.5 mm; (3) Boundary conditions: high‐speed bur idling or complete penetration of bone (PT). The milling speed was set at 0.5 mm/s, the milling angle was 45°, and the bur diameter was 4 mm. The vibration or sound was extracted by Fast Fourier Transform (FFT) in the frequency domain of the first nine harmonics to generate the feature vector in 9 dimensions (9‐D) space. These vibration and sound features were combined to form an 18‐D multi‐perception spatial vector for subsequent analysis, including five machine learning algorithms: Support Vector Machine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Decision Tree (DT), and deep learning models: Long Short‐Term Memory networks (LSTM).ResultsBased on the 18‐D features of tactile and auditory multisensory fusion, the LSTM model is trained using 6600 sets of high‐speed bur milling data. In order to achieve the best performance, a layer‐by‐layer parameter optimization strategy was used to determine the optimal parameter configuration, and finally, a single‐layer LSTM with 12 memory units was constructed. In terms of accuracy and stability, the model is significantly superior to the machine learning algorithms (SVM, KNN, NB, LDA, and DT), and the accuracy of LSTM is 99.32% in the milling states identification of cervical lamina milling with high‐speed bur.ConclusionThrough theoretical analysis and experimental verification, the study built a multi‐perception fusion framework based on tactile and auditory perception and accurately identified the cervical vertebra milling states through the LSTM model, which can provide perception means for operational spinal surgery robots in the future.

One of the most destructive natural hazard is an earthquake. Both the built environment and significant lives are lost. Due to presence of subduction zone and continuous continent to continent collision, major earthquakes in India are concentrated along the seismo-tectonically active Himalayan arc. Seismicity along the Alpine Himalayan Belt varies in well-defined patterns which are based on different parameters such as distance from tectonic source, presence of other historic earthquakes, intersections of tectonic features (in numbers), river length, presence of water body, meandering of rivers, soil conditions. An earthquake catalogue consisting of 540 earthquakes from year 1552 to 2020 is prepared for the consideration of data set. 117 tectonic features are considered for the study. For extracting feature a buffer of radius 25 km is considered along each earthquake of the catalogue. Four features namely distance from nearest tectonic feature, magnitude of earthquake, number of earthquakes within the buffer zone and presence of rivers (length) is extracted. A combination of these features allows identifying patterns based on training and classification of the entire data set. This paper is an attempt to compare four classification algorithms namely Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), K nearest neighbors (KNN) and Artificial Neural Networks (ANN) and present an integrated predictive model for identifying seismically susceptible zones (SSZ) around the Kangra Region. The seismicity in the study area is divided into two classes: class A and Class B based on the magnitude of earthquakes and are called as training data set for the classification algorithms. The training data set provides a basis for classifying a set of data into class A and Class B based on the extracted features. After classification the study area is the classified into two classes: Class A: Significant seismically susceptible area, Class B: less Susceptible zone. A comparative analysis is done in terms of accuracy of classifying an earthquake into the correct class as per training data set. Of the four classification methodologies, ANN showed the best performance by reducing the error in misclassifying the epicenters from either class. The errors accuracy of classifying an earthquake in the correct group were found to be 24.74%, 6.45%, 7.78%, 2.9% for LDA, SVM, KNN and ANN respectively.

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method.

Introducing two Random Forest based methods for cloud detection in remote sensing images

The surface electromyogram (sEMG) contains a wealth of motion information, which can reflect user's muscle motion intentions. The decoding based on sEMG has been widely used to provide a safe and effective human-computer interaction (HCI) method for neural prosthesis and exoskeleton robot control. The motor intention decoding based on low sampling frequency sEMG may promote the application of wearable low-cost EMG sensors in HCI. Therefore, a motor intention decoding scheme suitable for low frequency EMG signal is proposed in this paper, that is, transfer learning based on Alexnet. Moreover, the effects of different feature extraction methods and data augmentation with Gaussian white noise are fully analyzed. The proposed algorithm is evaluated with the NinaPro database 5. The highest accuracy can reach 70.4%±4.36% in 53 gestures identification of 10 subjects. Some classical machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA) and K Nearest Neighbor (KNN) are chosen to make comparison, where the SVM with Gaussian kernel function reaches to the maximum accuracy of 67.98%±4.56%. Two-way variance results show significant differences between each other. The experiment results show that the transfer learning is effective for decoding low-frequency sEMG for a large number of gestures.

Job shop scheduling is one of the complex problems in the manufacturing industry, such as an automotive body manufacturer. This manufacturing company, located in Cikarang, Indonesia, deals with huge customer demand that leads to difficulties in production scheduling. This will cause a delay in some jobs and product deliveries. The current system is using the semi-active scheduling approach and requires 637 minutes for performing 6 jobs with 5 machines. The genetic algorithm (GA) model is proposed as an alternative solution to solve this problem. The GA parameter is set as follow: The population size expected is 30 with maximum generation can be produced in amount of 50. The crossover rate, mutation, and preservation are set to 0.3, 0.1, and 0.1, respectively. After 50 generations are obtained, the optimum solution is shown in generation 6 with a makespan of 597 minutes. Thus, the genetic algorithm model is effectively reducing the makespan of the job-shop scheduling problem by 10% compared to the current method applied at the company.

Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology

The aim of this article is to measure the performance metrics of an accuracy, sensitivity and specificity of the Novel Support Vector Machine (SVM) algorithm for diabetics detection and compared with a K Nearest Neighbour (KNN) algorithm. Materials and Methods: A total of 768 samples are collected from diabetics detection datasets found in kaggle. These samples are divided into a training dataset (n=500) and test dataset (n=268). Results: By comparing the Novel Support Vector Machine with KNN. The SVM has achieved better accuracy and specificity than the KNN and the KNN has better Sensitivity than the SVM algorithm. The output values of accuracy, specificity and sensitivity of the SVM are 79%, 93% and 58%. The output values of accuracy, specificity and sensitivity of the KNN algorithm are 63%, 39% and 100%. The significance value is Pi0.05. The G power is taken as 0.8. Conclusion: The output values of accuracy, specificity and sensitivity of the Novel SVM are 79%, 93% and 58%. The output values of accuracy, specificity and sensitivity of the KNN algorithm are 63%, 39% and 100

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-‘2011’, ‘Miraj-‘08’, and ‘Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.