SVM Algorithm Research Articles

Implementation Strategies and Quality Evaluation Systems for Community College Guided Pathways Programs

Abstract The “Guided Pathways” program in community colleges can promote the efficient completion of students’ studies without restricting their choices and promote the popularization of education. This paper proposes a specific strategy for the implementation of the Guided Pathway program, and proposes a quality evaluation model for the Guided Pathway program based on the Sparrow Search Algorithm and Deep Neural Networks to construct an evaluation system. In the construction of the quality evaluation model, transient optimization perturbations are added to the SSA to improve the diversity, searchability, and robustness of the population position transformation. DNN cannot retain all the valuable features, so they are optimized to form DRNN, which is the quality evaluation model based on ISSA-DRNN. In terms of overall performance, the ISSA-DRNN algorithm in this paper has a prediction accuracy of more than 98% in different datasets, and the training and testing time required for evaluation is also significantly shorter than that of SVM and DRNN algorithms, resulting in superior overall performance. The quality evaluation of the Guided Pathways program was conducted at a community college in District S, City Q, China. The primary and secondary indicators have mean values between [3.4,3.7] and [3.4,3.6] that fall short of the respondents’ expectation of more than 4 points and cannot meet the demand. The implementation of Guided Pathways in S community college has deficiencies, and it should be improved and optimized by combining them with the implementation strategies proposed in this paper.

A novel brain EEG clustering based on Minkowski distance to improve intelligent epilepsy diagnosis

This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG feature selection for classification and have efficient Particle Swarm Optimization (PSO) in the context of machine learning. Given the intricacy of high-dimensional medical datasets, feature selection plays a critical role in preventing disease and promoting public health. By employing Minkowski clustering, the objective is to group dataset records into two clusters exhibiting high feature coherence, thereby improving accuracy by applying optimization techniques like PSO to select the most optimal features. Furthermore, the proposed model can be extended to intelligent datasets, including EEG and others. As fewer features are needed for precise categorization, intelligent feature selection is an advanced step of machine learning. This paper investigates the key factors influencing feature selection in the EEG Bonn University dataset. The proposed system is compared against various optimization and feature selection methods, demonstrating superior performance in analyzing and classifying EEG signals based on accuracy measures. The experimental results have confirmed the effectiveness of the suggested model as a valuable tool for EEG data classification, achieving up to 100% accuracy. The outcomes of this research have the potential to benefit medical experts in related specialties by streamlining the process of identifying and diagnosing brain disorders. Technically, the machine learning algorithms RF, KNN, SVM, NB, and DT are employed to classify the selected features.

Cultural Creative Product Design Methods under the Path of User Empathy

Abstract As consumption upgrades, China’s cultural and creative industry is experiencing rapid growth. However, user empathy remains lacking in some products. This paper explores a design method for these products based on empathy theory. It employs the TF-IDF algorithm to extract semantic features from product texts and uses the SVM algorithm for classification. Post-classification, the LDA theme model analyzes sentiment, integrating both visual and semantic models to enhance product design. An analysis of cultural and creative products using data analysis software reveals that approximately 79.5% of user comments exhibit positive sentiment, with an average sentiment score of 2.9218 and a peak score of 39.1363. This suggests strong positive emotional responses from nearly 80% of users. The proposed method effectively enhances user-product interaction and empathy.

Mining the Performance Characteristics of Yao Nationality Musical Instruments under Multivariate Statistical Analysis

Abstract In the context of the rapid development of modernization and urbanization, the inheritance and research of music and musical instruments by ethnic minorities are facing great challenges. It is, therefore, of enormous significance to uncover the performance characteristics of ethnic minority musical instruments. This paper focuses on four categories of Yao musical instruments, namely reed instruments, air-pipe instruments, wind instruments, and percussion instruments. We mine the performance features of Yao musical instruments using MFCC feature extraction, perceptual linear prediction parameter extraction, and other methods. The performance of this paper’s algorithm is explored by comparing the detection and recognition accuracy of its algorithm with SVM on four types of musical instruments. The performance characteristics of Yao musical instruments are explored by analyzing the timbre characteristics, auditory characteristics, and beat statistics of the four instruments. This paper’s algorithm significantly outperforms the SVM algorithm in the recognition correct rate of four different types of musical instruments, with differences of 2.1905%, 7.1574%, 5.3758%, and 3.6962%, respectively. The extracted performance features of Yao music instruments reveal that reed instruments and air-pipe instruments have a superior timbral effect than wind instruments and percussion instruments. The sound of pneumatic instruments and wind instruments is the best when it comes to audibility, and the beats of reed instruments and wind instruments are the best among the four instruments.

Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system

Agriculture forms a major occupation in countries like India. More than 75% people rely on farming for their daily wages. Food security on a global scale is mostly dependent on agriculture. Hence, achieving good yield in the crops grown by farmers is the major concern. Various environmental factors have a significant impact on the crop yield. One such component that contributes majorly to the crop yield is soil. Due to urbanization and enhanced industrialization, the agricultural soil is getting contaminated, losing fertility, and hindering the crop yield. One exciting new way to maximise crop yields while decreasing input costs is precision farming, which makes use of machine learning (ML) and the IoT. Machine Learning (ML) is employed for agricultural data analysis. The goal of this research is to optimise agricultural practices by presenting an integrated crop and fertiliser recommendation system. The proposed ML based model “Precision Agriculture” aims at predicting the suitable crops that can be grown based on the class which the soil sample belongs to and suggests the fertilizers that can be used to further enhance the fertility of soil. Using proposed model, farmers can make decisions on which crop to grow based on the soil classification and decide upon the nitrogen–phosphorous– potassium (NPK) fertilizers ratio that can be used. Comparison of the SVM algorithm with Naive Bayes, and LSTM has shown that SVM performed with a higher accuracy. Decision support tools that integrate AI and domain knowledge are provided by the study, which is a substantial contribution to precision agriculture.

Optimization model for mineral composition data analysis and its application in jade classification

Abstract The classification of jade grade has always been a very critical part of the jade industry, and improving the accuracy of jade grade classification is of great significance to the sustainable development of the jade industry. The study constructs a mineral identification classification model based on Raman spectroscopy + PCA through Raman spectroscopy and PCA principal component analysis and analyzes the data of jade grades and constituents. The actual performance of this paper’s model is explored by comparing its effectiveness with other algorithmic models in jade classification and the accuracy of classification parameters. The model in this paper is feasible in classifying the four grades of Hetian jade (seed material, gobi material, shanliushui material, and shanmu material). Green dense jade’s main minerals are <unk>-quartz and a few other minerals, including albite, hematite, graphite, and tourmaline. The main compositions of the sample jade are SiO2, Al2O3, and K2O. The overall accuracy of this paper’s model in classifying Xinjiang Hotan jade grades is 97.9%, which is significantly higher than that of the KNN classification algorithm and SVM classification algorithm. The total accuracy of this paper’s model on each parameter of jade grade is 85, which is higher than the 60 of the KNN algorithm and the 62 of the SVM algorithm, and the classification accuracy grade is high.

Design and Planning of Tourism Path Based on Social Media Sharing Data Mining

Abstract In recent years, along with the rapid development of artificial intelligence, big data and social media, informatization in the tourism industry also shows an explosive trend. This paper constructs a tourism path planning system based on data mining technology and the selection method of the optimal path. The GS algorithm is used to optimize the SVM algorithm to form the GS-SVM fusion algorithm, which makes the tourism path planning and predicts the optimal path according to the specific conditions of the journey, the characteristics of the scenic spot itself, and the tourists’ needs. After testing, this system has a good prediction performance on the traffic accessibility, attraction congestion and crowd change of scenic tour path. It is found that the transportation accessibility of scenic tour paths is positively correlated with tourists’ experience. In addition, in the experiment on the advantages and disadvantages of tourism paths, the passage time of paths 14, 15 and 16 is more than 3 minutes. Still, the actual length of these three paths is not more than 350m, which indicates that there are things for tourists to visit and experience on the passage paths, thus lengthening the passage time. This shows that the system provides real-time and reference paths for tourists by mining social media sharing data.

Intelligent Analysis Method of E-Commerce Data Based on Multiple Machine Learning Algorithms

Machine learning algorithm is one of the most popular research fields in the 21st century, and it is widely used in various industries. This paper first discusses the development status, characteristics and intelligent classification technology of e-commerce based on various methods. Then, through statistical analysis of multiple samples, this paper establishes a model to predict the quantity of goods and other time-related changes in the future period of time, and proposes an improved variance estimate to achieve the best effect under different data scenarios. Finally, the experiment proves that the response time of the linear regression algorithm fluctuates between 20ms and 25ms, and the overall fluctuation is small. For different samples, the response time of the linear regression algorithm is maintained in a relatively stable range, showing the stability of different input data. The response time of the decision tree algorithm fluctuates between 28ms and 33ms, showing a certain fluctuation. For different samples, the response time of decision tree algorithm fluctuates greatly, which may be affected by the characteristics of input data and show different processing efficiency. The response time of support vector machine algorithm fluctuates between 14ms and 18ms, and the change is relatively stable. For different samples, the response time of SVM algorithm is kept at a relatively stable level, showing the consistency of input data processing ability.

A machine learning based approach for 5G network security monitoring

Abstract This paper constructs a 5G network security detection system based on the functional requirements of network security detection and the P2DR process model. The structure can be broken down into three layers from the bottom to the top: acquisition layer, analysis layer, and display layer. The design focuses on writing the SVM algorithm into the software of the communication network security vulnerability monitoring system, and in order to solve the defect of the long training time of the model of this machine learning algorithm, the incremental learning vector machine model is used, which is combined into the CSV-KKT-ISVM model. Test datasets that cover system performance, effectiveness, and leakage are used to test the system after it is completed. The test data was analyzed to prove that the system’s memory usage was maintained at 46M, CPU usage was 5% to 10%, and the response time was no later than 1 s. The monitoring accuracy was 98.5% at the highest but decreased with the increase of the percentage of vulnerability data, and the accuracy dropped to 93.9% at 50%, the minimum was not lower than 90%, and the error rate was no less than 0.8%. To achieve the best outcome, the system threshold should be set to 5, and there should be no false alarms or misreporting.

Fall Detection for Elderly People Using Machine Learning

Abstract: Falling down is among the most common causes of medical attention required by the elderly people . Elderly people often injure themselves from falling down more especially when they are living alone. After a fall occurs, medical attention needs to be provided promptly in order to reduce the risk of victim . Several technologies have been developed which utilize webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for. Fall is one the major cause of death for older people. Detecting the fall plays a major role in saving lives. There are three different types of fall detection commonly used , such as wearable, ambient sensor and vision-based methods.If elderly people falls then it will put severe effect on their health and technology is helping humans in every aspect of their life and in this paper author is using machine learning algorithm to predict FALL scenarios by analysing their movements. In propose paper author has used SVM and Decision Tree algorithms to train SISFALL dataset and this trained model can be used to predict fall scenarios from new test data. Sensors will be embedded with elderly people’s body and this sensor will record their movement such as Heart Rate, EEG and circulation and then give this input to ML model and ML model will predict current scenario or posture and alert to elderly people

Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.

Comparative analysis of decision tree and support vector machine algorithm in sentiment classification for birds of paradise content

This research aims to analyze public sentiments towards National Geographic's content on the bird of paradise from the perspective of nature-based tourism. The method utilized is CRISP-DM, comprising stages of business understanding, data understanding, modeling, evaluation, and deployment. Focusing on sentiments expressed in response to National Geographic's Bird of Paradise content, this study seeks insights into how the public perceives and values nature-oriented tourism experiences. Comparing the results of DT and SVM algorithms with and without the SMOTE reveals noteworthy differences in classification performance. Without SMOTE, both DT and SVM exhibit relatively lower accuracy and AUC values compared to their counterparts with SMOTE. For DT, adding SMOTE substantially improves accuracy (from 92.44% to 95.20%) and AUC (from 0.517 to 0.956), indicating enhanced classification accuracy and model robustness. In addition, SVM demonstrates significant performance gains with SMOTE, achieving notably higher accuracy (from 92.12% to 98.63%) and AUC (from 0.617 to 0.999). The significantly higher values across various performance metrics for SVM underscore its effectiveness in handling imbalanced datasets and accurately classifying sentiment data. Therefore, researchers and practitioners may consider leveraging SVM for sentiment analysis tasks in similar contexts to achieve optimal classification results and enhance decision-making processes.

Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding

In the short term, a fully autonomous level of machine intelligence cannot be achieved. Humans are still an important part of HCI systems, and intelligent systems should be able to “feel” and “predict” human intentions in order to achieve dynamic coordination between humans and machines. Intent recognition is very important to improve the accuracy and efficiency of the HCI system. However, it is far from enough to focus only on explicit intent. There is a lot of vague and hidden implicit intent in the process of human–computer interaction. Based on passive brain–computer interface (pBCI) technology, this paper proposes a method to integrate humans into HCI systems naturally, which is to establish an intent-based HCI model and automatically recognize the implicit intent according to human EEG signals. In view of the existing problems of few divisible patterns and low efficiency of implicit intent recognition, this paper finally proves that EEG can be used as the basis for judging human implicit intent through extracting multi-task intention, carrying out experiments, and constructing algorithmic models. The CSP + SVM algorithm model can effectively improve the EEG decoding performance of implicit intent in HCI, and the effectiveness of the CSP algorithm on intention feature extraction is further verified by combining 3D space visualization. The translation of implicit intent information is of significance for the study of intent-based HCI models, the development of HCI systems, and the improvement of human–machine collaboration efficiency.

IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC

The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%.

Maturity stages detection prototype device for classifying custard apple (Annona squamosa L) fruit using image processing approach

Custard apple (Annona squamosa L.) is commercially grown for its sweetness, delicacy, and nutraceutical benefits across the world. Fruit must be harvested from trees before the climacteric stage begins for stabilizing the maturation process and perishability, which otherwise restricts longer shelf–life. Therefore, maturity–based grading or classifying custard apple fruit in accordance with their ripening is required for commercial marketing and industrial storage purposes. This is a crucial task that agriculturists and the food processing industries must have to perform manually, which is ineffective and susceptible to human errors. In this research, we have proposed an image processing–based maturity stages detection prototype device for fruit classification of custard apple (Cv. Balanagar) based on changes in their physical, chemical and imaging features (skin color of fruit areoles). The newly developed handheld device comprises of a Raspberry Pi board, camera module, LCD touch screen, and image processing algorithms to identify five essential maturity stages i.e. 0 %, 25 %, 50 %, 75 % and 100 % areoles opening of custard apple. The images were pre–processed and segmented, after which imaging aspects of custard apple fruit including relative R, G and B components were extracted and their relationship with chemical properties such as TSS was established. For classification of captured images, K–means clustering and SVM algorithms were performed on Raspberry Pi Platform with model training and classifier codes in Python (3.9.6) to get desired results. The statistical analysis revealed a substantial difference between the maturity stages in terms of R, G and TSS. Overall, 100 % accuracy was accomplished with respect to the grading of custard apple fruit at varied maturity. This could be a low–cost device useful for farm–level custard apple fruit classification, minimising economic losses caused by faulty supply entering the marketing chain. With slight modifications in algorithms, this device could also be used to assess the maturity of other fruit crops. It could be also an alternative to several expensive approaches being used for automating the maturity grading process that is not feasible, particularly for farmers working in field conditions.

Retracted: Research on Classification Method of Network Resources Based on Modified SVM Algorithm

Retracted: Research on Classification Method of Network Resources Based on Modified SVM Algorithm

Sentiment Analysis of 2024 Presidential Candidates Election Using SVM Algorithm

Elections for presidential candidates are held every 5 years with various candidates, especially on Twitter, arguments about political matters often occur that many Twitter users participate in discussions about the election for presidential candidate. Therefore, this study focuses on sentiment analysis to infer user responses to the presidential election and validate it by looking for a correlation between electability survey results and Twitter sentiment data using Pearson Correlation. In sentiment analysis model, the 10-Fold Cross Validation method is used to find the best model from a dataset with a division of training data and test data with 90:10 split. Then the alphabetic data will be converted into numeric data using the TF-IDF weighting method. To validate the best model, Confusion Matrix is used to get the best f1-score. The model is using Support vector machine algorithm with the Gaussian RBF (Radial Basis Function) kernel. The results of the analysis are compared with the results of the news portal electability survey which contains the 3 candidates using Pearson Correlation. This study produces the best fold for each data on each presidential candidate with the f1-score to find the best model for each fold. In the Peason Correlation result, the higher positive sentiment of each presidential candidate, the higher electability survey data. For further research, research can be discuss about hyper tuning parameters and using other kernels on Support vector machine algorithm.

Realistic Challenges and Optimization Approaches of Civic Education in Colleges and Universities in the Context of Big Data

Abstract Civic and political education with big data should not just be a one-way knowledge inculcation. It is more of a guideline and direction that can guide everyone’s life. This paper gives a detailed description of the realistic challenges facing Civic and Political Education in the context of big data and gives the optimization path of Civic and Political Education in combination with big data technology. The essence of ideological education is to help students establish correct concepts and values of life. This paper uses the PSO algorithm to improve the K-means algorithm, builds a student portrait based on student data, and fully analyzes the behavior related to student life consumption. Then, through the FOA algorithm to optimize the parameters of the SVM algorithm, the student learning data for deep mining in order to achieve the student academic early warning, to help college administrators understand the student learning situation. The purpose of Civic Education is to aid students in establishing correct concepts of life and learning, and this paper conducts corresponding empirical analysis. The results show that the PSOK-means algorithm achieves absolute convergence after 45 iterations, and its MSE value is 2.208, the FOA-SVM algorithm has 0.0979s more computing time than the RF-SVM algorithm after 2000 iterations, and the clustering of students’ portraits results in 4 classes, and the accuracy of academic warning is more than 80%. Reasonable big data technology and the deep integration of the application of political education in colleges and universities can help college administrators understand the development of students so as to formulate a political education program that is more in line with the development of students and to improve the quality of talent cultivation in colleges and universities.

Breast Cancer Detection in Thermographic Images Using Hybrid Networks

Introduction: Breast cancer is the most common cancer in women that causes more deaths than other cancers. Thermography is one of the methods of breast cancer diagnosis. The most important challenge in early detection of these images can be human error or lack of access to a skilled person. The use of artificial intelligence methods in image processing can be effective in early detection and reduction of human error. The main aim of this research was to introduce hybrid networks for intelligent diagnosis of breast cancer from thermographic images. Method: The thermographic images used in this study were collected from the DMR -IR database. First, the main features of the images were extracted by deep convolutional network (CNN). Then, FCNNs and SVM algorithms were used to classify breast cancer from thermographic images. Results: The accuracy rate for CNN_FC and CNN -SVM algorithms was 94.2% and 0.95%, respectively. In addition, the reliability parameters for these classifiers were calculated as 92.1%, and 97.5%, and the sensitivity for each of these classifiers as 95.5%, and 94.1%, respectively. Conclusion: The proposed model based on the deep hybrid network has good accuracy compared to similar algorithms; therefore, it can help doctors in the early diagnosis of breast cancer through thermographic images and minimize human error.

Categorization of Indian residential consumers electrical energy consumption pattern using clustering and classification techniques

Data Mining techniques are widely applied in power sector to analyze the electricity consumption pattern(ECP) of consumers for implementing effective demand response management. In this study, the ECP of LV consumers of Karur city is used for evaluation. The dataset consists of five years ECP. In this study, K-means clustering algorithm is used to categorize the data into binary and three classes. The results of the K-means clustering technique is used for developing eight classifier models. The hyperparameters of the classifier models are tuned using grid search optimization technique. The classification process applies hold-out partitioning and stratified 10-fold cross-validation(CV) techniques to evaluate the classifier’s efficiency in predicting a consumer’s ECP. The experimental result shows that the K-means clustering has minimal overlapping of 0.48% and 1.98% for binary and three-class clustering processes. Hence, the K-means algorithm results are used to build this study’s classification model. In the classification process, the experimental results of LR and SVM algorithms have better accuracy in binary and three class categories respectively, for the hold-out partitioning method. Meanwhile, the SVM also has better generalization ability and is validated using stratified 10-fold CV, achieving an accuracy of 99.09% and 98.19% for binary and three classes respectively, in the classification of consumer data.