Category Archives: Uncategorized

NIRBHAY: Analysis and Prediction of Crime
Authors:-Ms. Sayali Angat, Mr.Kapil Gharat, Mr.Angarak Gurav, Ms. Harshala Patil, Professor Prachi Sorte

Abstract-The increasing complexity and scale of urban areas requires new crime analysis and prediction strategies The research project focuses on harnessing the power of machine learning algorithms to analyze historical crime data and predict future crime in specific areas. This study explores the potential of predictive analytics for crime prevention and law enforcement using clinical data that includes various crime types, demographic data, and characteristics of the area. This approach involves pre-processing the dataset to resolve missing values, outliers, and feature engineering to extract relevant information. To find patterns and correlations between variables, a range of learning models such as support vector machines, random forests, and decision trees are fed into the data. Utilize measures like accuracy, precision, recall, and F1 score to assess the performance of the model. Furthermore, the actual offense is incorporating predictive models into internet platforms in order to allow users to get predictions based on characteristics like time, location, and publicly accessible data. The platform provides law enforcement, legislators and urban planners with information to better allocate resources and prevent crime. The results demonstrate the effectiveness of the machine learning process in crime analysis and prediction, with high accuracy in predicting crime scenes. The use of the website promotes easy access and usability, providing participants with the skills to reduce crime and increase public safety in the city.

DOI: 10.61137/ijsret.vol.10.issue2.140

Published by: vikaspatanker

Unveiling the Complexities of Behavioral Finance: Understanding Human Decision Making in Financial Markets
Authors:-Preeti Padma Sahu, Aniket Burman

Abstract-Behavioral finance is a subject of finance that studies the impact of psychological factors on how people make financial choices. This study intends to make valuable contributions to the expanding body of research in behavioral finance by exploring the behavioral biases and heuristics that affect investors’ decision making processes. Through a comprehensive review of existing literature, key concepts such as loss aversion, overconfidence, and herding behavior are examined, providing insights into the irrational behavior observed in financial markets. The objective of this research is to empirically analyze the impact of behavioral biases on investment decisions using a mixed methods approach combining qualitative and quantitative analysis. Data is collected through surveys and interviews with investors, supplemented by quantitative analysis of market data. The findings reveal significant correlations between certain behavioral biases and investment outcomes, shedding light on the complexities of human behavior in financial decision making. The conclusions drawn from this study underscore the importance of understanding and mitigating behavioral biases to enhance investment performance and promote financial wellbeing. This research contributes to both theoretical understanding and practical applications in the field of behavioral finance, offering valuable insights for investors, financial practitioners, and policymakers alike.

DOI: 10.61137/ijsret.vol.10.issue2.139

Published by: vikaspatanker

Social Media’s Impact on Food Industry
Authors:-Palak Sinha, Mohd. Faisal, Krishna Rajput, Ms. Tanya Sharma

Abstract-This in-depth analysis examines the profound impact that social media has had on consumer behavior and how it has changed the food industry. The unique connection between advanced stages and the culinary scene is analyzed, featuring the crucial job of virtual entertainment in forming dietary inclinations, dietary patterns, and feasting decisions. The review researches the meaning of client created content, powerhouse advertising, and arising advancements in reshaping the food environment, with suggestions for nourishment, culinary culture, and monetary strengthening. Tending to scholastics, industry experts, and policymakers, this paper offers important bits of knowledge into the developing computerized age food industry.

10.61137/ijsret.vol.10.issue2.138

Published by: vikaspatanker

Effect of Guided-Inquiry Teaching Method on Academic Performance of Philippine Public Secondary School Grade 12 Students: A Quasi-Experimental Research
Authors:-Jomar P. Flores, Nivea Louwah D. Sermona

Abstract-This study investigated the effect of the guided inquiry teaching method on the academic performance of Philippine public secondary school grade 12 students in Electrical Installation and Maintenance. A quasi-experimental design was adopted for the study. A pre-test and post-test were conducted for both control and experimental groups. The instruments used for data collection were 30 objective questions tagged as the Electrical Installation and Maintenance Achievement Test (EIMAT). The instrument was validated by five experts in the field. To determine the instrument’s reliability, Cronbach’s alpha formula was used and a reliability coefficient of 0.84 was obtained. Means and standard deviations were used to analyze the descriptive data, while the null hypothesis was tested using a t-test at a 0.5 level of significance. Findings revealed that students taught with the guided-inquiry teaching method performed better with higher post-test mean scores than those taught using the lecture-demonstration teaching method. Also, findings indicated that the guided-inquiry teaching method makes the students perform better in terms of their performance skills than the lecture-demonstration teaching method. Given the findings, it was recommended, among others, that the guided-inquiry teaching method be adopted in technical colleges and secondary schools for instruction in EIM to improve the academic performance of the students.

DOI: 10.61137/ijsret.vol.10.issue2.137

Published by: vikaspatanker

Evaluation of Strength Properties in Geopolymer Bricks Blended with Rice Husk Ash and M Sand
Authors:-Kavipriya S, Kamalini V, Kanishkaa C, Amala P

Abstract- This paper presents a parametric experimental studies which investigate the effects of using geopolymers (such as fly ash and alkaline activator solution), rice husk ash, M-sand and sawdust in the production of bricks. This project mainly focus towards the production of unburnt bricks to replace normal conventional burnt clay bricks and this could be achieved by polymerization process under ambient curing conditions. This product enhance in reduction of ecological pollution by reducing emission of CO2 concentration. Rice husk ash and saw dust are the organic wastes that can be recycled for use in construction industry without producing any harm to human and environment in a great manner. Size of bricks to be casted is about 22 x 7.5 x 7.5cm. Both strength and durability properties such as compressive strength, water absorption, efflorescence tests are to be carried out in this project to check the reliability of geopolymer bricks with normal conventional bricks and flyash bricks.

DOI: 10.61137/ijsret.vol.10.issue2.301

Published by: Kajal Tripathi

Opportunities and Threats in a Smart Grid Setting for AI-Enabled Demand Response
Authors:-Ryan Ed

Abstract-One of the most essential commodities for modern humans is electricity. The notions of smart grids with demand response were established to tackle problems and obstacles in the transfer of power via the conventional grid. Wind turbines, microgrids, and defect detectors are just a few of the power generating, transmission, and distribution components that contribute to the massive amounts of data produced everyday by these systems. Smart electric appliances and meters also play a role in load control. New developments in computers and big data have made it possible to use Deep Learning (DL) to forecast electrical consumption and peak hours by discovering patterns in the produced data. Inspired by the potential benefits of deep learning for smart grids, this article aims to provide a thorough overview of how DL is being used for intelligent smart grids with demand response. Here we lay down the groundwork for deep learning, smart grids, demand response, and the reasoning behind all of this. Second, we take a look at the most recent developments in deep learning’s use to smart grids and demand management, including topics like electric load predicting, state estimation, energy theft identification, energy sharing, and trading. Furthermore, we demonstrate the usefulness of DL via a range of applications and use scenarios. Lastly, we draw attention to pressing concerns and possible future possibilities in the application of DL to smart grids and demand response, as well as the difficulties already encountered in the literature.

DOI: 10.61137/ijsret.vol.10.issue2.159

Published by: Kajal Tripathi

Strategic Data Management: Frameworks, Implementation Challenges, and Success Stories
Authors:-Jorge White

Abstract- In the current landscape where digital innovation shapes every aspect of business operations, the adept management and strategic use of data stand as pivotal factors in securing organizational prosperity and a competitive edge. This study ventures into the realm of data strategy, shedding light on its pivotal role in the digital age and its profound influence on the operational dynamics of contemporary organizations. It outlines a variety of methodologies for the formulation of a holistic data strategy, encompassing essential facets such as governance, data quality, architectural integrity, and organizational data literacy. Furthermore, this research identifies prevalent obstacles encountered during the deployment of data strategies and furnishes actionable strategies derived from the successful experiences of leading organizations. By engaging in a comparative scrutiny of diverse strategic models and probing into the avant-garde trends shaping data management, this paper prognosticates the trajectory of data strategy evolution and its repercussions for future business models. Offering an amalgam of theoretical constructs, empirical challenges, and illustrative success narratives, the paper serves as a comprehensive guide for entities aiming to optimize their data strategy endeavors, thereby maximizing the utility of their digital information reservoirs.

DOI: 10.61137/ijsret.vol.10.issue2.136

Published by: vikaspatanker

Advancing Ethical and Accurate Hate Speech Detection with Machine Learning Techniques
Authors:-Jorge White

Abstract- In recent years, the proliferation of social media platforms has significantly increased, providing a digital space where individuals from diverse backgrounds can express their opinions and thoughts. This surge in social media usage has brought to light the challenge of managing and moderating hate speech—a form of content that can incite violence, discrimination, and hostility. The primary difficulties in detecting and processing hate speech stem from the linguistic diversity of users, the nuanced usage of language that can alter meanings based on context, and the scarcity of robust datasets for the development and evaluation of detection models. This paper explores these challenges in depth and proposes an innovative approach to enhance the efficiency and effectiveness of hate speech detection. We critically analyze the limitations inherent in current methodologies and introduce a model based on Support Vector Machine (SVM) algorithms. Our comparative analysis demonstrates that SVM-based models offer superior performance in detecting hate speech compared to conventional neural network approaches. This is attributed to the SVM’s ability to handle high-dimensional data and its effectiveness in classifying complex, nuanced linguistic patterns. Furthermore, we delve into the technical and ethical implications of automating hate speech detection. The paper discusses the ongoing challenges in balancing accuracy with the need for ethical considerations, such as avoiding censorship and respecting free speech. We address the technical hurdles related to algorithmic bi as, model interpretability, and the need for continuous adaptation to evolving language and social norms. In conclusion, while significant strides have been made in employing machine learning techniques for hate speech detection, several critical issues remain unresolved. Our research underscores the importance of interdisciplinary efforts, combining insights from linguistics, social sciences, and computer science, to develop more sophisticated, ethical, and effective hate speech detection systems. By advancing the use of SVM and exploring its potential in this domain, we contribute to the broader discourse on making digital platforms safer and more inclusive.

DOI: 10.61137/ijsret.vol.10.issue2.135

Published by: vikaspatanker

Exploring the Potential of Unlisted Shares: An Analysis of Performance and Shareholding Patterns in the Indian Market
Authors:-Shravani Gambhire, Prerna Telge, Sneha Soundararaj, Neha Sahu

Abstract- This study explores the untapped potential of unlisted shares as an investment avenue, conducting a thorough analysis of their performance within a closed market environment. Focused on thirty-three diverse unlisted Indian companies across sectors like Banks & NBFC, manufacturing, IT, and insurance, the research utilizes a stratified random sampling approach. Secondary data from annual reports and company records supplement primary data collected through interviews. The analysis reveals sector-specific trends, emphasizing the strong performance of manufacturing and insurance while highlighting challenges in the banking, financial, and IT sectors. The study underscores significant public shareholding in the unregulated market, indicating its accessibility to retail investors. Despite liquidity challenges and information disparities, the unlisted market presents alternative investment opportunities for those strategically navigating its dynamics. In conclusion, the research not only provides insights for diversification-seeking investors but also raises awareness among policymakers about the unlisted market’s role in the broader investment landscape, emphasizing the need for informed decision-making in a volatile yet potentially lucrative market.

DOI: 10.61137/ijsret.vol.10.issue2.134

Published by: vikaspatanker

The Potential of AI in Enhancing Education Access and Quality
Authors:Vinayak Patil, Neeraj Prajapat, Ranmeet Kour Bhatia, Darshana Jain

Abstract- This paper explores the use of artificial intelligence (AI) in education, with a focus on its intersection with e-learning, digital technologies, intelligent internet, and digital literacy. AI has the potential to transform the educational landscape by providing personalized learning experiences, virtual tutors, and intelligent evaluation systems. The paper also examines the importance of digital literacy in ensuring that students are prepared to navigate the digital world and make informed decisions. However, the integration of AI in education also poses challenges such as bias, privacy concerns, and the need for ethical AI practices. This paper suggests interdisciplinary partnerships and ethical AI practices as potential solutions to address these issues. Overall, the paper highlights the potential of AI in enhancing education and promoting digital literacy, while also emphasizing the importance of responsible and ethical implementation. In summary, this article adds to the expanding research on the intersection of AI and sustainability, highlighting the significance of utilizing AI’s capacity for change to create a future that is fairer and more sustainable.

DOI: 10.61137/ijsret.vol.10.issue1.133

Published by: vikaspatanker