JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}