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Dr. Rohan Singh
No full textDr. Rohan Singh is presently working as Associate Professor, Chandigarh University, Punjab. He had received his Ph.D. from Siksha \u27O\u27 Anusandhan (Deemed to be University), Bhubaneswar in Management. His primary research areas include human resource management and organizational behaviour. He has published his research in various reputed international journals and conferences, with around 300 citations.He has more than 14 years of experiencein Project Management at Sponsored Research & Industrial Consultancy Cell, IIT Kharagpur.https://www.interscience.in/mentors/1109/thumbnail.jp
Study on the Impact of Private Enterprises\u27 Participation in the Mixed Reform of State-owned Enterprises on the Value Preservation and Appreciation of State-owned Assets
Full text linkState-owned listed companies in the commercial category in Shanghai and Shenzhen A-shares from 2013 to 2020 were selected as the research sample. Data related to the shareholding and delegated behavior of private shareholders among the top ten shareholders are utilized. Using a fixed-effects model, we empirically analyze the impact of private companies\u27 participation in the mixed reform of state-owned enterprises on the value preservation and appreciation of state-owned assets at the equity level and the management right level. Explore the moderating effects of internal control and the level of market-oriented development on the above relationships. The study shows that the participation of private enterprises in the mixed reform of SOEs can effectively contribute to the value preservation and appreciation of state-owned assets, both at the equity level and at the management level. Meanwhile, the level of internal control of SOEs has a significant positive moderating effect on the relationship between the participation of private firms in SOEs\u27 mixed reform and the value-added of SOEs\u27 assets; The participation of private firms can effectively compensate for the impact of insufficient market development on the value-added of SOEs\u27 assets
Deep Learning-based Gated Recurrent Unit Approach to Stock Market Forecasting: An Analysis of Intel\u27s Stock Data
Full text linkThe stock price index prediction is a very challenging task that\u27s because the market has a very complicated nonlinear movement system. This fluctuation is influenced by many different factors. Multiple examples demonstrate the suitability of Machine Learning (ML) models like Neural Network algorithms (NN) and Long Short-Term Memory (LSTM) for such time series predictions, as well as how frequently they produce satisfactory outcomes. However, relatively few studies have employed robust feature engineering sequence models to forecast future prices. In this paper, we propose a cutting-edge stock price prediction model based on a Deep Learning (DL) technique. We chose the stock data for Intel, the firm with one of the quickest growths in the past ten years. The experimental results demonstrate that, for predicting this particular stock time series, our suggested model outperforms the current Gated Recurrent Unit (GRU) model. Our prediction approach reduces inaccuracy by taking into account the random nature of data on a big scale
AN EXPERIMENTAL INVESTIGATION OF MULTI-CYLINDER CONVENTIONAL CI ENGINE USING MADHUCA INDICA OIL AS FUEL
Full text linkThe present work is mainly discussed with a qualitative study of engine’s significant characteristics fuelled with mahua bio-diesel & its different types of mixtures with neat diesel. The significant technical properties of various mixtures are tabulated. A 4-S multi-cylinder (6-Cylinder) DI conventional CI engine is used for the study under different speed modes. All types of characteristics for various mixtures are estimated in running the engine. Pure diesel is indicated by B-0 and pure mahua bio-diesel is represented by B-100. From the test results, it is found that B-25 gives almost the same BTE as B-0 at maximum load, compared to all the blends. The blend B-0 and B-25 give the least SFC of 0.332 and 0.268 kg/kWh at minimum speed (1200 rpm) and maximum speed (2400 rpm) at maximum load as contrasted to all mixtures. The B-100 gave 3.01% of NOx while related to B-0 @ lower speed
INSIGHTQUEST FROM DATA
Full text linkData mining is the process of discovering useful patterns and insights from large datasets, using statistical and machine learning techniques. It involves extracting knowledge from data and transforming it into an understandable structure for further use. Data mining algorithms can be used to analyze various types of data such as text, images, and videos, and can be applied to various domains such as finance, healthcare, and marketing. Data mining has many practical applications, such as customer segmentation, fraud detection, predictive modeling, and recommendation systems. It has become an important tool for businesses and organizations to gain insights from their data and make data-driven decisions. However, it also raises concerns about privacy, data protection, and ethics, as it involves handling large amounts of sensitive data. Therefore, ethical and responsible use of data mining techniques is crucial to ensure the protection of individual rights and the preservation of social values
An Experiment To Analyze The Impact of Steam On The Productivity of The RF STALAM 85 kW Yarn Dryer
Full text linkRadio Frequency has been one of the most commonly used technologies in the processing industries like textile, rubber, paper and many other industries for drying purposes. The RF STALAM 85kW is a machine used to dry the yarn obtained after the dyeing and hydro extraction process. This machine uses radio frequency to remove the moisture content from the dyed yarn. In this research paper, I would like to share the findings of an experiment conducted on the RF STALAM 85kW yarn dryer machine. In this experiment, I have three trials each with and without the steam at variable conveyor belt speeds. At the end of the experiment, I calculated the per day production to conclude the optimum speed to run the RF STALAM 85kW yarn drier machine to obtain maximum production with and without steam
Ergonomic Risk Assessment of Maintenance Workers in Educational Institute
Full text linkRisks associated with ergonomics are pervasive groups that have crept into people\u27s daily lives. Ergonomic risks have been linked to long-term health effects like musculoskeletal disorders, cumulative trauma disorders, and lower back pain. Due to the limitations of their jobs, workers are most impacted by these repetitive, continuous labor activities. The main objective of this project is to provide a solution to the ergonomic problems that construction workers in the educational maintenance industry encounter. Based on the conditions, only a few jobs were identified, and samples were obtained by watching and asking people about their jobs. For tasks like plantation, rebar bending, material transportation, etc., the RULA and REBA method is used in this project. This study aims to investigate the ergonomic hazards experienced by educational maintenance construction workers
A Systematic Review of Hybrid Renewable Energy Systems About Their Optimization Techniques with Analytic Hierarchy Process
Full text linkHybrid renewable energy systems are the fastest growing power sector worldwide. The drawbacks of most hybrid energies were identified by the previous researchers such as space/sizes and costs of the system. This review article aims to find out the best optimization techniques for HRES by using the Analytic Hierarchy Process (AHP). More than 100 plus papers were taken to do this review. Among all the top energy journal publications considered for these reviews such as ELSEVIER -54.9%, SPRINGER-6.9%, MDPI-5.9%, TAYLOR AND FRANCIS-5%, IEEE ACCESS-13%, and others -15%. Thus, the expected result of this review is that the researchers acknowledge their decision-making to choose the best optimization techniques and hybrid renewable energies
Editorial
Full text linkThis special issue seeks papers that provide a convergent research perspective on business futures, i.e., research that draws on many disciplinary views and strives to establish fresh integrative frameworks and vocabularies. Addressing the difficulty of work culture and intelligent machines in a broad sense necessitates grappling with complicated issues such as motivation, cognition, machine learning, human learning, and system design, among others
Improvement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning – A Quantitative Analysis
Full text linkAI and Machine learning are playing a vital role in the financial domain in predicting future growth and risk and identifying key performance areas. We look at how machine learning and artificial intelligence (AI) directly or indirectly alter financial management in the banking and insurance industries. First, a non-technical review of the prior machine learning and AI methodologies beneficial to KPI management is provided. This paper will analyze and improve key financial performance indicators in insurance using machine learning (ML) algorithms. Before applying an ML algorithm, we must determine the attributes directly impacting the business and target attributes. The details must be manually mapped from string values to fit the model and its required datatypes for applying these specific features to an ML model. We propose hashing to convert string values to numeric values for data analysis within our model. After the string values are hashed, we can introduce our model. In our case, we have chosen to use a decision tree model. Decision Trees are beneficial for this use case as this algorithm generates rulesets that govern the target value output. These rulesets can then be applied to the financial dataset and infer the “best fit” value that might be wrong/missing. Finally, because of the model, we can use this most accurate data version to detect general ledger transactional data patterns