For the prediction of any stock price and its fluctuations in prices, researchers have suggested several versions of machine learning techniques. Machine learning-based techniques fail to achieve good prediction and in turn, their accuracy is not adequate to predict the stock price. For sentiment analysis related to the financial domain BERT model is quite useful. The score generated by BERT is useful to get more insight. Few research works which have incorporated financial news, have not used financial corpus for training and testing. FinBERT is quite useful to solve stock pricing fluctuations as it is specially trained on corpus related to the financial domain. The stock market usually gets fluctuated during any impactful news either positive or negative sentiments. In this work, highly fluctuating stock price movement is predicted efficiently which is validated by experiment analysis. Further, in existing research works, stock prices are predicted for a specific company only. In this paper, A hybrid method to predict fluctuations in stock prices has been suggested using FinBERT and Long Short-term Memory (LSTM) along with news that impacted the market. The proposed method using news score and hybrid approach outperforms existing approaches significantly.
{"title":"An approach for predicting the price of a stock using deep neural network","authors":"D. Pandey, Megha Jain, Kavita Pandey","doi":"10.47974/jios-1412","DOIUrl":"https://doi.org/10.47974/jios-1412","url":null,"abstract":"For the prediction of any stock price and its fluctuations in prices, researchers have suggested several versions of machine learning techniques. Machine learning-based techniques fail to achieve good prediction and in turn, their accuracy is not adequate to predict the stock price. For sentiment analysis related to the financial domain BERT model is quite useful. The score generated by BERT is useful to get more insight. Few research works which have incorporated financial news, have not used financial corpus for training and testing. FinBERT is quite useful to solve stock pricing fluctuations as it is specially trained on corpus related to the financial domain. The stock market usually gets fluctuated during any impactful news either positive or negative sentiments. In this work, highly fluctuating stock price movement is predicted efficiently which is validated by experiment analysis. Further, in existing research works, stock prices are predicted for a specific company only. In this paper, A hybrid method to predict fluctuations in stock prices has been suggested using FinBERT and Long Short-term Memory (LSTM) along with news that impacted the market. The proposed method using news score and hybrid approach outperforms existing approaches significantly.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article discusses the challenges of accurately predicting the direction of the stock market and proposes a new approach using machine learning and manual forecasting. The article explores the use of technical analysis and machine learning to predict current stock market indices’ values by training on historical data. The authors demonstrate how these methods can be used to influence investor judgments at different levels of consideration, including unrestricted, near, medium, high, and volumic. The article also explores the use of social media platforms like Twitter and the correlation between stock prices and local weather patterns to improve forecasting accuracy. The authors present their research in three phases, demonstrating the potential of machine learning and technical analysis to provide accurate and reliable predictions for investors seeking to protect themselves from market volatility.
{"title":"Predicting stock prices with LSTM: A hybrid machine learning model for financial forecasting","authors":"G. Shukla, Nitin Balwani, Santosh Kumar","doi":"10.47974/jios-1416","DOIUrl":"https://doi.org/10.47974/jios-1416","url":null,"abstract":"This article discusses the challenges of accurately predicting the direction of the stock market and proposes a new approach using machine learning and manual forecasting. The article explores the use of technical analysis and machine learning to predict current stock market indices’ values by training on historical data. The authors demonstrate how these methods can be used to influence investor judgments at different levels of consideration, including unrestricted, near, medium, high, and volumic. The article also explores the use of social media platforms like Twitter and the correlation between stock prices and local weather patterns to improve forecasting accuracy. The authors present their research in three phases, demonstrating the potential of machine learning and technical analysis to provide accurate and reliable predictions for investors seeking to protect themselves from market volatility.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Women of childbearing age may face infertility owing to polycystic ovarian syndrome (PCOS).This illness causes ovarian dysfunction, which increases the chance of miscarriage and stillbirth, thus early management is necessary for a healthy lifestyle and to avoid future infections. Weight gain, irregular menstruation cycles, thinning hair, acne, dark and thick spots on the back of the neck, and anxiety disorders are the main symptoms of PCOS. A single out of five women has PCOS. Often women ignore the common symptoms of PCOS and wait until pregnancy problems emerge to get care. Considering PCOS is associated with an increased risk of developing a number of illnesses, such as glucose intolerance, , elevated cholesterol levels, and cardiovascular diseases, it should be identified as soon as feasible. The current tools and therapies are insufficient to identify and forecast PCOS at an earlier stage. To address this issue, we developed a model that will aid in the early detection of PCOS utilizing machine learning techniques and an absolute and minimal set of parameters. The Extra Tree Classifier, a forward selection approach followed by Wrapper, the Chi-square test, and Pearson Correlation was employed as selection criteria to evaluate essential characteristics. KAGGLE has a database that is used for training and testing.
由于多囊卵巢综合征(PCOS)，育龄妇女可能面临不孕不育。这种疾病会导致卵巢功能障碍，从而增加流产和死胎的机会，因此早期治疗对于健康的生活方式和避免未来感染是必要的。体重增加，月经周期不规律，头发稀疏，痤疮，颈后黑斑和厚斑，焦虑障碍是PCOS的主要症状。五分之一的女性患有多囊卵巢综合征。女性常常忽视多囊卵巢综合征的常见症状，直到怀孕问题出现才得到治疗。考虑到多囊卵巢综合征与许多疾病的发病风险增加有关，如葡萄糖耐受不良、胆固醇水平升高和心血管疾病，应尽早确定。目前的工具和治疗方法不足以在早期阶段识别和预测多囊卵巢综合征。为了解决这个问题，我们开发了一个模型，该模型将利用机器学习技术和绝对最小参数集帮助PCOS的早期检测。Extra Tree Classifier是一种前向选择方法，随后采用Wrapper、卡方检验和Pearson相关性作为评估基本特征的选择标准。KAGGLE有一个用于培训和测试的数据库。
{"title":"A prediction model for poly-cystic ovary syndrome problem using computational intelligence","authors":"D. Pandey, Kavita Pandey, Budesh Kanwer","doi":"10.47974/jios-1414","DOIUrl":"https://doi.org/10.47974/jios-1414","url":null,"abstract":"Women of childbearing age may face infertility owing to polycystic ovarian syndrome (PCOS).This illness causes ovarian dysfunction, which increases the chance of miscarriage and stillbirth, thus early management is necessary for a healthy lifestyle and to avoid future infections. Weight gain, irregular menstruation cycles, thinning hair, acne, dark and thick spots on the back of the neck, and anxiety disorders are the main symptoms of PCOS. A single out of five women has PCOS. Often women ignore the common symptoms of PCOS and wait until pregnancy problems emerge to get care. Considering PCOS is associated with an increased risk of developing a number of illnesses, such as glucose intolerance, , elevated cholesterol levels, and cardiovascular diseases, it should be identified as soon as feasible. The current tools and therapies are insufficient to identify and forecast PCOS at an earlier stage. To address this issue, we developed a model that will aid in the early detection of PCOS utilizing machine learning techniques and an absolute and minimal set of parameters. The Extra Tree Classifier, a forward selection approach followed by Wrapper, the Chi-square test, and Pearson Correlation was employed as selection criteria to evaluate essential characteristics. KAGGLE has a database that is used for training and testing.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70471032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Chitre, K. Wanjale, Aradhanaa Deshmukh, Shyamsunder P. Kosbatwar, Anup Ingle, Sheela N. Hundekari
In computer music, instrument recognition is a critical part of sound modeling. Pitch, timbre, loudness, duration, and spatialization are all components of musical sounds. All of these components play a significant part in determining the quality of the tonal sound. It is possible to alter the first four parameters, but timbre always poses a challenge [6]. It was inevitable that timbre would take center stage. Musical instruments are distinguished from one other by their distinct sound quality, independent of their pitch or volume. To distinguish between monophonic and polyphonic music recordings, this method might be used. In Musical Information Retrieval, classification plays one of the critical role. Monophonic instrument classification can be found in literature with quiet a substantial combinations of features and classifiers. Polyphonic instrument classification witnessed less references in the literature and is still an area to be explored specifically when it comes to Indian Classical domain. The present paper exactly focusses on this experimentation. Several Indian instruments were used to produce training data sets for the proposed approach’s evaluation purposes. Among the instruments utilized are the flute, harmonium, and sitar. Statistical and spectral factors are used to classify Indian musical instruments along with the Artificial Intelligence-based methods. Hybrid features from multiple domains that extract essential musical properties are extracted. Accuracy is demonstrated through an Indian Musical Instrument SVM and GMM classification. With monophonic sounds, SVM and Polyphonic produce an average accuracy of 89% and 91%. GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33, according to the results of the studies. The future scope of this recognition framework can be an Artificial Intelligence System with a system linked with the Industrial Internet of Things (IIOT) framework to develop a standalone system or application which can be used for real- time classification of instruments.
{"title":"Artificial intelligence-based classification performance evaluation in monophonic and polyphonic indian classical instruments recognition with hybrid domain features amalgamation","authors":"A. Chitre, K. Wanjale, Aradhanaa Deshmukh, Shyamsunder P. Kosbatwar, Anup Ingle, Sheela N. Hundekari","doi":"10.47974/jios-1345","DOIUrl":"https://doi.org/10.47974/jios-1345","url":null,"abstract":"In computer music, instrument recognition is a critical part of sound modeling. Pitch, timbre, loudness, duration, and spatialization are all components of musical sounds. All of these components play a significant part in determining the quality of the tonal sound. It is possible to alter the first four parameters, but timbre always poses a challenge [6]. It was inevitable that timbre would take center stage. Musical instruments are distinguished from one other by their distinct sound quality, independent of their pitch or volume. To distinguish between monophonic and polyphonic music recordings, this method might be used. In Musical Information Retrieval, classification plays one of the critical role. Monophonic instrument classification can be found in literature with quiet a substantial combinations of features and classifiers. Polyphonic instrument classification witnessed less references in the literature and is still an area to be explored specifically when it comes to Indian Classical domain. The present paper exactly focusses on this experimentation. Several Indian instruments were used to produce training data sets for the proposed approach’s evaluation purposes. Among the instruments utilized are the flute, harmonium, and sitar. Statistical and spectral factors are used to classify Indian musical instruments along with the Artificial Intelligence-based methods. Hybrid features from multiple domains that extract essential musical properties are extracted. Accuracy is demonstrated through an Indian Musical Instrument SVM and GMM classification. With monophonic sounds, SVM and Polyphonic produce an average accuracy of 89% and 91%. GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33, according to the results of the studies. The future scope of this recognition framework can be an Artificial Intelligence System with a system linked with the Industrial Internet of Things (IIOT) framework to develop a standalone system or application which can be used for real- time classification of instruments.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today, detecting waste, collecting it, processing it, and getting rid of it are among the most significant environmental issues in developing and undeveloped counties. It has been observed that a large amount of garbage remains strewn on the roadside. This study presented a garbage detection technology such as machine learning and gadgets connected to the Internet of Things (IoT), such as an IP-enabled CCTV camera, to take pictures and send them to the city’s main server. The input images are transformed into a two-dimension array of integers using Python modules and divided into the garbage and no garbage classes. There is an 80:20 split between the training and testing datasets from the input dataset. Preprocessed images are then utilised as inputs for a wide range of machine learning and neural network models for classification; these include K-Nearest Neighbour (KNN), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). The test data sets are applied, and a confusion matrix is formed for all models to analyse the efficiency and performance of the trained models. Results from the confusion matrix are contrasted with those from the area under the Receiver characteristics operating curve (AUC). As a result, the ConvNet model is best suited for classifying garbage or no garbage present in open space, and the LR model proposed best suits the garbage detection problem. The proposed models are best suitable for improving the efficiency of existing garbage identification systems and developing a new system for smart cities.
{"title":"Machine learning and IoT-based garbage detection system for smart cities","authors":"R. Sharma, Manisha Jailia","doi":"10.47974/jios-1349","DOIUrl":"https://doi.org/10.47974/jios-1349","url":null,"abstract":"Today, detecting waste, collecting it, processing it, and getting rid of it are among the most significant environmental issues in developing and undeveloped counties. It has been observed that a large amount of garbage remains strewn on the roadside. This study presented a garbage detection technology such as machine learning and gadgets connected to the Internet of Things (IoT), such as an IP-enabled CCTV camera, to take pictures and send them to the city’s main server. The input images are transformed into a two-dimension array of integers using Python modules and divided into the garbage and no garbage classes. There is an 80:20 split between the training and testing datasets from the input dataset. Preprocessed images are then utilised as inputs for a wide range of machine learning and neural network models for classification; these include K-Nearest Neighbour (KNN), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). The test data sets are applied, and a confusion matrix is formed for all models to analyse the efficiency and performance of the trained models. Results from the confusion matrix are contrasted with those from the area under the Receiver characteristics operating curve (AUC). As a result, the ConvNet model is best suited for classifying garbage or no garbage present in open space, and the LR model proposed best suits the garbage detection problem. The proposed models are best suitable for improving the efficiency of existing garbage identification systems and developing a new system for smart cities.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear combination of different base kernels as target kernel function is designed to classify chest Xray images. Multiple kernels are used here to exploit their non-linear mapping capability on heterogeneous data. MKD-ELM is an enhanced classifier, which does not require iterative training of its parameters. The proposed technique has better feature extraction along with non-iterative training, thus it is having very fast training and very good generalization performance. The kernel and regularization parameters that influence how accurate MKD-ELM is at classifying data, are tuned through experimentation. So, an optimization technique called the genetic algorithm (GA) has been utilized to determine the ideal combination of these parameters for improved performance. The performance of the proposed technique is analysed for COVID-19 detection problem using chest Xray (ChXR) images by changing the training set, types of kernels and coefficients used for combining base kernels. The proposed algorithm achieves a 97.27% recognition rate on first dataset which comprises 5,856 images and 99.06% on the second dataset which consists of 13,808 images. A higher recognition rate is attained for these ChXR image datasets, in respect to modern techniques demonstrating the effectiveness of the proposed algorithm.
{"title":"Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images","authors":"Arshi Husain, Virendra P. Vishvakarma","doi":"10.47974/jios-1319","DOIUrl":"https://doi.org/10.47974/jios-1319","url":null,"abstract":"In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear combination of different base kernels as target kernel function is designed to classify chest Xray images. Multiple kernels are used here to exploit their non-linear mapping capability on heterogeneous data. MKD-ELM is an enhanced classifier, which does not require iterative training of its parameters. The proposed technique has better feature extraction along with non-iterative training, thus it is having very fast training and very good generalization performance. The kernel and regularization parameters that influence how accurate MKD-ELM is at classifying data, are tuned through experimentation. So, an optimization technique called the genetic algorithm (GA) has been utilized to determine the ideal combination of these parameters for improved performance. The performance of the proposed technique is analysed for COVID-19 detection problem using chest Xray (ChXR) images by changing the training set, types of kernels and coefficients used for combining base kernels. The proposed algorithm achieves a 97.27% recognition rate on first dataset which comprises 5,856 images and 99.06% on the second dataset which consists of 13,808 images. A higher recognition rate is attained for these ChXR image datasets, in respect to modern techniques demonstrating the effectiveness of the proposed algorithm.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Context retrieval and ranking have always been an area of interest for researchers around the world. The ranking provides significance to the data that has to be presented in front of users but it also consumes time if the ranking architecture is not organized. The retrieval is dependent upon the co-relation among the data attributes that are supplied against a class label also referred to as ground truth and the ranking depends upon the sensing polarity that indicates the hold of the outcome towards asked information. This paper illustrates an ontological architecture that involves two phases namely context retrieval and ranking. The ranking phase is composed of three different algorithm architectures namely k-means, Support Vector Machines (SVM), and Deep Neural Networks (DNN). The DNN is tuned to fit and work as per the availability of a total number of samples. The proposed work has been evaluated for both quantitative and qualitative parameters in different sets and scenarios. The proposed work has also been compared with other state of art techniques and is illustrated in the paper itself.
{"title":"An ontological architecture for context data retrieval and ranking using SVM and DNN","authors":"Pooja Mudgil, Pooja Gupta, Iti Mathur, Nisheeth Joshi","doi":"10.47974/jios-1347","DOIUrl":"https://doi.org/10.47974/jios-1347","url":null,"abstract":"Context retrieval and ranking have always been an area of interest for researchers around the world. The ranking provides significance to the data that has to be presented in front of users but it also consumes time if the ranking architecture is not organized. The retrieval is dependent upon the co-relation among the data attributes that are supplied against a class label also referred to as ground truth and the ranking depends upon the sensing polarity that indicates the hold of the outcome towards asked information. This paper illustrates an ontological architecture that involves two phases namely context retrieval and ranking. The ranking phase is composed of three different algorithm architectures namely k-means, Support Vector Machines (SVM), and Deep Neural Networks (DNN). The DNN is tuned to fit and work as per the availability of a total number of samples. The proposed work has been evaluated for both quantitative and qualitative parameters in different sets and scenarios. The proposed work has also been compared with other state of art techniques and is illustrated in the paper itself.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Dadheech, Vijay H. Kalmani, S. R. Dogiwal, V. Sharma, Ankit Kumar, S. Pandey
Breast cancer is one of the most prevalent diseases in India’s urban regions and the second most common in the country’s rural parts. In India, a woman is diagnosed with breast cancer growth every four minutes, and a woman dies from breast cancer sickness every thirteen minutes. Over half of breast cancer patients in India are diagnosed with stage 3 or 4 illness, which has extremely low survival rates; hence, an urgent need exists for a rapid detection strategy. To forecast if a patient is at risk for breast cancer, we utilise the classification techniques of machine learning, in which the machine learning model learns from the previous information and can anticipate on the new information that is generated by the data. To create a model using Logistic Regression, Support Vector Machines, and Random Forests, this dataset was collected from the UCI repository and studied in this study. The primary goal is to improve the accuracy, precision, and sensitivity of all the algorithms that are used to categorise data in terms of the competency and viability of each and every algorithm. Random Forest has been shown to be the most accurate in classifying breast cancer, with a precision of 98.60 percent in tests. The Scientific Python Development Environment is used to carry out this machine learning study, which is written in the python programming language.
乳腺癌是印度城市地区最常见的疾病之一，也是该国农村地区第二大常见疾病。在印度，每4分钟就有一名女性被诊断出患有乳腺癌，每13分钟就有一名女性死于乳腺癌。在印度，超过一半的乳腺癌患者被诊断为3期或4期，生存率极低;因此，迫切需要一种快速检测战略。为了预测患者是否有患乳腺癌的风险，我们利用机器学习的分类技术，其中机器学习模型从以前的信息中学习，并可以预测由数据生成的新信息。为了使用逻辑回归、支持向量机和随机森林来创建模型，本研究从UCI存储库中收集了该数据集并进行了研究。主要目标是根据每个算法的能力和可行性来提高用于对数据进行分类的所有算法的准确性、精度和灵敏度。随机森林已被证明是乳腺癌分类最准确的方法，在测试中准确率达到98.60%。本机器学习研究使用Scientific Python Development Environment进行，使用Python编程语言编写。
{"title":"Breast cancer prediction using supervised machine learning techniques","authors":"P. Dadheech, Vijay H. Kalmani, S. R. Dogiwal, V. Sharma, Ankit Kumar, S. Pandey","doi":"10.47974/jios-1348","DOIUrl":"https://doi.org/10.47974/jios-1348","url":null,"abstract":"Breast cancer is one of the most prevalent diseases in India’s urban regions and the second most common in the country’s rural parts. In India, a woman is diagnosed with breast cancer growth every four minutes, and a woman dies from breast cancer sickness every thirteen minutes. Over half of breast cancer patients in India are diagnosed with stage 3 or 4 illness, which has extremely low survival rates; hence, an urgent need exists for a rapid detection strategy. To forecast if a patient is at risk for breast cancer, we utilise the classification techniques of machine learning, in which the machine learning model learns from the previous information and can anticipate on the new information that is generated by the data. To create a model using Logistic Regression, Support Vector Machines, and Random Forests, this dataset was collected from the UCI repository and studied in this study. The primary goal is to improve the accuracy, precision, and sensitivity of all the algorithms that are used to categorise data in terms of the competency and viability of each and every algorithm. Random Forest has been shown to be the most accurate in classifying breast cancer, with a precision of 98.60 percent in tests. The Scientific Python Development Environment is used to carry out this machine learning study, which is written in the python programming language.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Naidu, Avinash Sharma, S. P. Diwan, V. Gowda, Parth M. Pandya, Anand Kumar Gupta
This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.
{"title":"Development of object identification model with deep reinforcement learning algorithm","authors":"P. Naidu, Avinash Sharma, S. P. Diwan, V. Gowda, Parth M. Pandya, Anand Kumar Gupta","doi":"10.47974/jios-1346","DOIUrl":"https://doi.org/10.47974/jios-1346","url":null,"abstract":"This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deepshikha Seth, Priyanka Agarwal, A. Vashisht, Deepak Bansal, Priti Verma
Organizations are increasingly evolving their workplace climate to accommodate the youngest generation with millennials slowly taking over leadership positions. Millennials have transformed the way businesses interact with workers by sheer force of numbers. India has one of the youngest demographics in the world, with post-millennials also starting to join the workforce. Studies have shown that millennials are different from the earlier generations in their work attributes. Some of their workplace expectations collide with the conventional workplace norms; yet many organizations have started to reshape their workplace strategies to provide more opportunities to the millennials. The COVID-19 pandemic pushed a Fast Forward button to these efforts and 2020 saw almost all the businesses promptly changing their working norms. Remote working, along with digital technology and flexi-hours – once characterized as the millennial work characteristics – became the new normal for everyone. Retaining tech-savvy employees has become a significant concern of business, and they are fighting for the best talent to overtake the now aging Gen X employees. As new ground realities of remote working hit us, this research seeks to gain an insight into the minds of senior-level managers who are facing the new class of workers. This study is an attempt to fulfil this gap in the industry and facilitate a more relatable work environment for the millennials.
{"title":"A study on the role of millennials in changing workplace dynamics: How millennials can help businesses move ahead in the post COVID-19 world","authors":"Deepshikha Seth, Priyanka Agarwal, A. Vashisht, Deepak Bansal, Priti Verma","doi":"10.47974/jios-1292","DOIUrl":"https://doi.org/10.47974/jios-1292","url":null,"abstract":"Organizations are increasingly evolving their workplace climate to accommodate the youngest generation with millennials slowly taking over leadership positions. Millennials have transformed the way businesses interact with workers by sheer force of numbers. India has one of the youngest demographics in the world, with post-millennials also starting to join the workforce. Studies have shown that millennials are different from the earlier generations in their work attributes. Some of their workplace expectations collide with the conventional workplace norms; yet many organizations have started to reshape their workplace strategies to provide more opportunities to the millennials. The COVID-19 pandemic pushed a Fast Forward button to these efforts and 2020 saw almost all the businesses promptly changing their working norms. Remote working, along with digital technology and flexi-hours – once characterized as the millennial work characteristics – became the new normal for everyone. Retaining tech-savvy employees has become a significant concern of business, and they are fighting for the best talent to overtake the now aging Gen X employees. As new ground realities of remote working hit us, this research seeks to gain an insight into the minds of senior-level managers who are facing the new class of workers. This study is an attempt to fulfil this gap in the industry and facilitate a more relatable work environment for the millennials.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：