查看更多>>摘要：Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.
查看更多>>摘要：With a high rate of morbidity and mortality, chronic kidney disease is a global health issue that also causes other diseases. Patients frequently overlook the condition because there aren't any evident symptoms in the early stages of CKD. An efficient and effective Extreme gradient boosting method for the early diagnosis of kidney illness has been proposed in this paper to explore the capability of various machine learning algorithms. DenseNet can extract a variety of features such as vector features. After that feature extraction phase, the data are fed into the feature selection phase. The features are selected based upon the Improved Salp swarm Algorithm (ISSA). The proposed CKD classification method has been simulated in PYTHON. Utilizing the CKD dataset from the UCI machine learning resources, the dataset is then tested. Sensitivity, accuracy, and specificity are the performance metrics used for the proposed CKD classification approach. The results of the experiments demonstrate that the proposed approach outperforms the present state-of-the-art method in classifying CKD.
ChauPattnaik, SampaRay, MitrabindaNayak, India Mitalimadhusmita
48-61页
查看更多>>摘要：The rapid advancement of computer technology motivates software developers to use commercial off-the-shelf software components for system growth. For particular architectural elements (for instance, components), the reliability criteria associated with testing-based conventional procedures are unknown. In the traditional reliability estimation, the probabilistic method is applied. The source data problem, which depends on a number of factors that may or may not correspond to the real working conditions of the system, is this technique's major shortcoming. The component -based software reliability estimation is based on a number of parameters, including the individual component reliability, transition probability, failure rate, etc. Fuzzy logic converts fuzzy data into useful information, making it easier to develop creative solutions for vague and uncertain concepts based on various factors that influence reliability. To assess the reliability of component-based systems, the authors provide a fuzzy logic technique, which has the ability to improve the question of uncertainty.
查看更多>>摘要：Online consumer reviews play a pivotal role in boosting online shopping. After Covid-19, the e-commerce industry has been grown exponentially. The e-commerce industry is greatly impacted by the online customer reviews, and a lot of work has been done in this regard to identify the usefulness of reviews for purchasing online products. In this proposed work, predicting helpfulness is taken as binary classification problem to identify the helpfulness of a review in context to structural, sentimental, and voting feature sets. In this study, the authors implemented various leading ML algorithms such as KNN, LR, GNB, LDA and CNN. In comparison to these algorithms and other existing state of art methods, CNN yielded better classification results, achieving highest accuracy of 95.27%. Besides, the performance of these models was also assessed in terms of precision, recall, F1 score, etc. The results shown in this paper demonstrate that proposed model will help the producers or service providers to improve and grow their business.
查看更多>>摘要：A large section of the population has a source of income from the agriculture sector, but their share in the Indian GDP is low. Thus, there is a need to forecast energy to improve and increase productivity. The main sources of energy in agriculture are electricity, coal, and diesel. Among them, electricity plays an important role in land irrigation. Power forecasting is also essential for demand response management. Thus, any process that dissolves future consumption is favorable. This article presents a time series-based technique for forecasting medium-term load in agriculture. The aim is to find the peak periods of power consumption by months and seasons using statistical and machine learning-based techniques. The result shows that SARIMA has lower RMSE and exponential smoothing has lower RMSPE error than random forest and LSTM, which makes the statistical approach more efficient than intelligent approach for historical datasets. The season-wise peak demand occurs during the Rabi season. Finally, five-year ahead load in the agriculture sector was determined using the best models.
查看更多>>摘要：In recent years, distributed generations (DGs) are extremely fast in detecting their location, which helps to satisfy the ever-increasing power demands. The placement of energy storage systems (ESSs) could be a substantial opportunity to enhance the presentation of radial distribution system (RDS). The major part of DG units in RDS deals with the detection of ideal placement and size of the DGs, which efficiently balance the power loss and voltage stability. The ideal location and size of ESSs are examined in standard IEEE-33 and 69 bus systems, which is important to reduce power losses. Nowadays, several algorithms or techniques are modified for the development of hybrid algorithms to improve the quality of DG allocation. In this research, a hybrid shuffled frog leap algorithm (SFLA) with ant lion optimizer (SFLA-ALO) is proposed for the optimal placement and size of the DG and ESS in the RDS to reduce power losses and maintain the stability of voltage. The performance of the proposed SFLA-ALO technique is compared with the implemented BPSO-SFLA technique.
查看更多>>摘要：Mathematical ranking plays a critical role in the era of the internet and bigdata. Google's PageRank is well-known as a trillion-dollar algorithm. Definitely, algorithmic ranking frameworks are found on every search engine. In this paper, the article shall investigate how PageRank can be applied in the blockchain space to build up reliable and verifiable social credit and reputation systems. It is expected to provide a measure of credibility complementary and parallel with FICO, which is not applicable for individuals lacking credit information in financial institutions. Moreover, the approach proposes an unbiased method of interpreting and measuring real social interaction and reputation ranking on a blockchain network. The authors envision a future of payment based on cryptocurrencies (especially stable coins) and digital fiats; thus the proposed credit scoring framework shall be helpful for P2P credit and lending networks, possibly for decentralized finance (Defi) applications.
查看更多>>摘要：Software engineering mainly aims to produce software of good quality that is delivered on time and on budget. Software quality becomes an important concern for quantifying the performance of software attributes. The seminal objective of the work is to choose the appropriate software quality model according to the client's needs where the client can give more importance to specific criteria compared to others as per his/her application's requirements. The proposed approach will help to decide the best alternative suitable for the application. The work is based on selecting the most suitable software quality model taking all the parameters into consideration while making the decision using multi-criteria decision-making techniques.
查看更多>>摘要：Recently, the research on sentimental analysis has been growing rapidly. The tweets of social media are extracted to analyze the user sentiments. Many of the studies prefer to apply machine learning algorithms for performing sentiment analysis. In the current pandemic, there is an utmost importance to analyze the sentiments or behavior of a person to make the decisions as the whole world is facing lockdowns in multiple phases. The lockdown is psychologically affecting the human behavior. This study performs a sentimental analysis of Twitter tweets during lockdown using multinomial logistic regression algorithm. The proposed system framework follows the pre-processing, polarity and scoring, and feature extracting before applying the machine learning model. For validating the performance of proposed framework, other three majorly used machine learning based models--namely decision tree, naive Bayes, and K-nearest neighbors-- are implemented. Experimental results prove that the proposed framework provides improved accuracy over other models.
查看更多>>摘要：Sentiment analysis is the process of identifying and categorizing opinions computationally to determine the attitude expressed in the spoken or written text as positive, negative, or neutral. Negation analysis is the task of analyzing the negative opinions by identifying the scope of negation within a sentence and applying linguistic or grammatical rules of the language. In this paper, the rules for identifying the scope of negation within a sentence and the rules applicable to different negation categories are defined. An algorithm by the name SentiNeg has been proposed for processing negations at the sentence level. SentiNeg algorithm filters non-opinionated sentences from the data to avoid unnecessary processing. For opiniated sentences, the algorithm applies different linguistic or grammatical rules of the language to identify negative opinions. SentiNeg algorithm takes opinionated sentences as input and provides a detailed aspect-based summary of negative opinions that are expressed on the entity under analysis.
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