The rising frequency of crimes against women necessitates the development of the accurate and effective methodology to enable targeted interventions and preventive measures. This research utilizes a dataset prepared from the National Crimes Record Bureau’s 2022 report on crimes against women. In this study, we investigated the application of advanced ensemble classification techniques to analyze the crime against women dataset. The spatial distribution of aggregated crimes is illustrated using a map of India, providing a visual representation of how crimes against women are distributed across the country. Our proposed approach employs a hybrid ensemble model that integrates the base learners as Light Gradient Boosting Machine, CatBoost, and AdaBoost, applied with the novel hybrid meta-learner. The novel hybrid meta-learner incorporates Random Forest and CatBoost as base learners, with logistic regression serving as meta-learner. By stacking these base learners and applying the novel hybrid meta-learner, this approach capitalizes on the strengths of each technique, overcoming their individual limitations and enhancing overall classification performance. We evaluate performance of baseline models, ensemble models, and our proposed hybrid ensemble model using metrics, accuracy, precision, recall, and F1-score. Our comparative analysis showed that the proposed hybrid model surpasses both baseline and conventional ensemble methods in classification accuracy and reliability. Additionally, the efficiency of the Meta_RCL model is validated through cross-validation, achieving an accuracy of 0.969, precision of 0.969, recall of 0.968, and F1-score of 0.968 which are the highest compared to other models. This model’s versatility allows it to be applied to other crime datasets, aiding law enforcement agencies, policymakers, and social organizations in identifying patterns, trends, and risk factors.

Rape and attempted rape incidents remain critical concerns both within the state and across the country, requiring detailed spatial and temporal assessment. This study examines district-level patterns across 33 districts from 2015 to 2022 using SaTScan’s spatial variation in temporal trends under a discrete Poisson model. Both linear and quadratic temporal models are applied to distinguish steady trends from non-linear dynamics. The results show consistently higher rape cases than attempted rape across districts, with Jaipur and Alwar exhibiting persistently high incidence counts. Four significant high-trend clusters were identified for both rape and attempt to rape. Bhilwara demonstrated the steepest annual increases in both rape (19.24

Crimes against women, including dowry deaths, miscarriage, and cruelty by husband or in-laws, remain a pervasive social issue in India. This study examines the spatiotemporal patterns and spatial autocorrelation of these crimes in Rajasthan, India, before, during, and after the COVID-19 lockdown. Leveraging data from the National Crime Records Bureau from 2018 to 2022, we employed spatial scan statistics to identify high-intensity hotspots and analyze their evolution. For spatial autocorrelation, Moran’s I Index was evaluated and a Logistic regression model was employed to identify factors affecting hotspot formation. Ajmer emerged as a major hotspot during the lockdown, while Bhilwara, Chittorgarh, Rajsamand, Bundi, Kota, and Ajmer remained hotspots before and after the lockdown. Spatiotemporal analysis revealed 2020 lacked the highest likelihood ratio for hotspot formation, and Moran’s I showed positive spatial autocorrelation pre- and post-COVID-19, with lower values during the lockdown. The modeling identified the factors associated with hotspot formation as the poverty rate, per capita income, unemployment, substance use by men, early pregnancy, and disability in females. These findings highlight the complex nature of the event, shaped by socioeconomic and demographic factors, and offer insights to guide targeted policies and resource allocation to combat the menace of crime against women in Rajasthan.

Road accidents and subsequent injuries and deaths are at alarming stage in India and the same is true for almost all part of the world with some variations in the cases. Continuous expansions of road networks, disproportionate increase in urbanization, enormous state of motorization and many other micro factors. Rising accidental deaths result into large number of losses of life especially between the age group of 15–50, imposes a great concern to all stake holders (from policy makers to common people). Road accidents are multi-causal events mainly categorized by human error, road resource optimization, effective policy formulation. As such we are motivated to set our objectives to compute the hotspot and coldspots to develop deep understanding of related characteristics. The second objective is to support the policy makers in optimal resource utilization needed to combat and evolve an effective strategy ranging from mass awareness campaign to technological innovations and developing the infrastructure to meet pre-and-post accident related challenges. To achieve the computational efficacy and drawing in-depth inferences we have implemented the software's namely MS Excel, saTScan and Python. The finding of the current work highlights southern states and northern states have disproportionality higher rate. We found cities like Coimbatore, Aurangabad, Chandigarh, Bangalore, Delhi, Chennai and Hyderabad as the urban hotspots based on computed values of the statistical likelihood ratio and risk ratio. We found them as the most likelihood clusters (primary hotspots). We found time series model model Holt-Winter method is the best model when compared to other exponential model.

Maternal and child health (MCH) is a critical indicator of overall societal well-being, particularly in developing countries. The study presents a comprehensive analysis to evaluate MCH in Uttar Pradesh (UP), a state facing significant public health challenges. Using National Family Health Survey (NFHS-5) data on MCH indicators, we developed a health index to systematically assess and compare health status across various districts. The multi-criteria decision-making (MCDM) technique, specifically Criteria Importance Through Inter-criteria Correlation (CRITIC) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to determine the importance of closeness of various criteria affecting MCH care, such as antenatal care, institutional delivery, postnatal care, immunization coverage and nutritional status. The overall performance of each district in terms of MCH was measured to develop a composite index based on the MCDM approach. In contrast, the indicator weights were determined by the CRITIC method. Some districts showed better performance on specific indicators, while others lagged. The resulting index provides a nuanced understanding of the regional disparities within UP, highlighting areas of urgent need for healthcare interventions. The findings underscore the importance of targeted policies and programs to improve MCH outcomes, contributing to the broader goals of sustainable development and public health enhancement in the region.. KEYWORDS :Maternal and child health, CRITIC, TOPSIS, Index.

Development and population are two crucial and complex areas of study for the researchers. They depend on many variables such as demography, economic status, nutritional status of the child and women, etc. This research aims to determine the best districts by evaluating them against eight specific criteria that reflect the demographic composition of women and children in Uttar Pradesh (UP). The identification of the criteria of the variables is determined by various factors such as education, security & threat, gender equality, and health dimensions within the districts of UP, India. To achieve this we attempted to implement the multiple criteria decision-making (MCDM) methods comprehensibly. This study has presented an impartial assessment of the performance of 75 districts in UP. The methodology included a technique for order preference by similarity to ideal solution (TOPSIS) and multi-objective optimization based on ratio analysis (MOORA). Data on demographic and educational parameters were collected from the most recent published report of the national family health survey (NFHS-5) and various online portals & platforms of the government of UP. Also, we made an attempt to validate the techniques using a non-parametric statistical test known as Wilcoxon sign rank test. TOPSIS and MOORA were identified as two most popular MCDM techniques for demography research. Interestingly, we found districts namely, (Agra, Kanpur Nagar, Moradabad), Lucknow and Shrawasti as outliers with respect to variables area(A3), CAW(A5) and TFR(A6) respectively that need to be dealt with careful attention and effective measures has to be taken. The study provides useful information on the demographic characteristics of districts in UP and possibly provide the basis to our policymakers for designing the targeted interventions to improve the social and economic indicators of the State.

Maternal and child health (MCH) is a critical indicator of overall societal well-being, particularly in developingcountries. The study presents a comprehensive analysis to evaluate MCH in Uttar Pradesh (UP), a state facing significantpublic health challenges. Using National Family Health Survey (NFHS-5) data on MCH indicators, we developed a healthindex to systematically assess and compare health status across various districts. The multi-criteria decision-making (MCDM)technique, specifically Criteria Importance Through Inter-criteria Correlation (CRITIC) and TOPSIS (Technique for Order ofPreference by Similarity to Ideal Solution) is used to determine the importance of closeness of various criteria affecting MCHcare, such as antenatal care, institutional delivery, postnatal care, immunization coverage and nutritional status. The overallperformance of each district in terms of MCH was measured to develop a composite index based on the MCDM approach. Incontrast, the indicator weights were determined by the CRITIC method. Some districts showed better performance on specificindicators, while others lagged. The resulting index provides a nuanced understanding of the regional disparities within UP,highlighting areas of urgent need for healthcare interventions. The findings underscore the importance of targeted policiesand programs to improve MCH outcomes, contributing to the broader goals of sustainable development and public healthenhancement in the region.

Nearly 10 million deaths from cancer are expected worldwide by 2020, making it the most common cause of death. India is anticipated to have 2.7 million cases. The count of 13.9 lakh new cases and 8.5 lakh deaths each year is highly disappointing. We proposed the application of scan statistics aiming at the identification of the hotspots of cancer incidence and mortality. The concept of hotspots allows us to develop an effective plan at the local and regional levels to combat the problems that are resulting from the concerning rise in cancer incidence and mortality. It is important to utilize spatio-temporal and longitudinal data to characterize the problem and evolve the strategy. The treatment of cancer is costly, and the consequential mental agony of the patient and their family is a great challenge to our society. The lack of health infrastructure for cancer treatment and non-optimal utilization of inadequate resources pose a great challenge and need to be resolved urgently. To address this issue, we found the hotspots of cancer incidence and mortality. We used SaTScan, MS Solver, R, SPSS, SAS, Tableau, and MS Excel to achieve computational efficacy. It is interesting to highlight that Kerala is the incidence hotspot, but Bihar is the mortality hotspot in India. The most frequently occurring cancer in males and females are the esophagus and larynx, respectively. The current work is relevant to individual patients as well as the concerned governments, policymakers, medical professionals, and all other stakeholders.

The liver is the most important and one of the largest organs in the body. The Liver serves a number of functions, making it vital to the human body. When the Liver's regular functions are disrupted, it becomes a disrupted Liver. The number of liver patients has been significantly growing in recent years, making improved liver disease detection a challenging aspect of health care. The use of an automated diagnostic system can assist in identifying liver disease and improve diagnostic accuracy. As a consequence, we have used machine learning classification techniques like Logistic Regression, Gaussian Naïve Bayes, Stochastic Gradient Descent, KNN, Decision Tree, Random Forest, and SVM to design a more accurate diagnostic model. All these algorithms have implemented on the ILPD dataset with the intension to reduce time of diagnosis and earlier prediction of disease. In Future the accuracy of classification will be enhanced by feature extraction and the big dataset can also be examined for training the model and determining algorithms.

Human history endorsed evidence of violence against women in different forms. Offense against women made an appearance in the early stages, and it is continuing. The present research based on an inferential methodology using scan statistics. It is one of the most extensively used statistical methods as one of the most popular emerging data science techniques in the study of events like crime. Moral degradation in human values, narrow-mindedness, growing intolerance, lack of value-based education, illiteracy, and unemployment are some of the critical factors, that is likely to be held responsible for offenders to perpetrate criminal activities that could lead to such a heinous and shameful crime event. The research is conducted based on the secondary data available on the data portal, gathered from different government sources. The geographical unit under the study is based on various crime zones adjusted to and embed into the revenue district of NCT of Delhi. The p-value obtained from the log-likelihood ratio for each crime district taken as the basis of identification and segregation of hotspot. The software used for computation is M.S. Excel, MS Solver, R-Studio, and SaTScan. The current work considered to be of great importance in the optimization of resources needed for crime control, monitoring, and surveillance to avoid such events in any form in future planning. The term optimization is used for maximal utilization of available scarce resources like installation of cameras, number of police deployments, distribution of various sophisticated policing equipment’s etc. to an extent of best possible measures to curb the menace effectively. Presently policing resources per person in NCT of Delhi is very much scarce and Delhi can be rated as very poor on that scale. For example, even in 2021 the estimated number of sanctioned strengths of Delhi Police is 83,762, means there are only 27 police sanctioned (not deployed) per ten thousand of the population. The actual number of deployed police per ten thousand populations may be further less. Similarly, we can cite an example of available CCTV cameras for surveillance estimated in the year 2021 is 1.32 lakh which comes to almost four CCTV for ten thousand person and even many of them are nonfunctional and defectives due to poor operational and maintenance reasons. Hence the problem of optimum utilization of resources is of prime importance. As such the relevance of the present work is aiming at societal interest and also enables our stressed policing bodies to execute an effective planning, is likely to be appreciated.

Stock price prediction attracts individual decisions to invest in share market and may encourage the common people to become active in share trading. Stock trading gives us direct monetary benefit under prevalent uncertainties. Factors like political developments at state, national and international levels, conservative, diverse and complex social conditions, natural disasters, famine, pandemics, economic trade cycle (recession, boom and recursion) and many others have great effects on share prices and stock cost. The present work is based on secondary data sources acquisitioned from various public and private data portals accessed freely. There have been various efforts made in the past to predict the trends of the stock prices on the basis of secondary data. The predicted confidence estimates may enable the common investors to make a profit despite large risk of loss at different point of time under the dynamics of market fluctuations. The present work is based on an inferential methodology, which has been found to be instrumental and particularly suitable to the financial time series analysis. Time series modelling and forecasting have fundamental importance in stock market prediction and analysis. Further, with the application of multivariate methods and regression modelling, we expect better forecast accuracy. The implementation of the proposed approach has been incorporated on real-time data set on daily basis. The software used for various computations is Python, SPSS, MS Excel and MS Solver. The current work is relevant in prediction and analysis of the stock market prices and also seems to be useful to a large number of peoples engaged in day to day trading.

In the current scenario, Internet of Things (IoT) is a very interesting and popular research field. It is well known that the Internet of Things (IoT) is a platform where a device becomes smarter day by day, processing becomes intelligent, and communication becomes informative in the field of IoT. The selection of protocol is a big issue. It is closely tied to the implementation of the protocols and the ecosystem that drives the protocols. Thus, making a decision regarding an appropriate protocol in an IoT scenario is very complex. Along with the implementation environment, we need to also consider all aspects of deployment, interoperability, maintainability security as part of the protocol selection. This review will examine the range of protocols suitable for IoT application-level scenarios, features of these protocols, and the specific requirements to build a robust system. We also try to compare these protocols and try to propose their hybrid approaches. This chapter describes the architectures of IoT, protocols used in IoT, its security issues, and applications of IoT.

Drowsiness of the driver is the significant cause of the road accidents. The need of the hour is to come up with some measures to control it, and our prototype helps us to achieve that. The main objective of this research study is to come up with a solution to curb down road accidents due to fatigue. Drowsiness can be detected through various ways, but we mainly focus on facial detection using computer vision. In this prototype, a driver’s face is captured by our program for analyzing. We apply facial landmark points with the help of a facial detection algorithm to extract the location of the driver’s eyes. Subsequently, the eye moment is recorded as per the specified frame; if the driver closes his eyes more frequently or more than a specified time, then he/she can be declared as drowsy which will eventually lead to triggering of the alarm. With the advancement of technology, automatic self-driving cars are emerging at a fast rate, but still, they need someone’s supervision so we can use the above-mentioned technology in those cars to see if a driver is sleepy or not. If he/she is sleepy, then the car can slow down and stop gradually, on its own and will not go further.

The history of suicide in India and particularly in Tamil Nadu has been witnessed since the existence of human settlement through ages, from pre-historic to ancient, medieval, modern and even to contemporary time.The primary objective of the present work is to identify the hotspots of suicide prevailing among four major cities of Tamil Nadu, namely, Chennai, Coimbatore, Madurai and Tiruchirappalli, for the time frame of 2012-2019.In addition to this, we are interested in descriptive and inferential analysis to find out some prominent attributes using multivariate statistical techniques.The present work analysed the Spatio-Temporal and longitudinal time series data acquired from National Crime Record Bureau (NCRB).Suicide hotspots due to various factors are detected using SaTScan.Despite metropolitan and cosmopolitan characteristics, high and dense urban population, proportionately higher cluster of illegal emigrants and settlements, Chennai being a capital city has not been identified as a hotspot of suicide for any factors under study.This study is useful and relevant for control and monitoring bodies in planning and curbing such a precious loss of human lives in the state of Tamil Nadu.

The entire E-Commerce department stores an abundant amount of data everyday which sometimes results in missing items, improper inventory control and thus loose the track of their database. This problem is not only restricted to them but the customers also plays a huge role in creating this scenario like updating the items in cart, leave the cart with items at any point which results in problems at checkout and often they cancel the orders. There is a dire need of a system which not only stores this fluctuating data but keep it in an effective way. This system keeps a good track of all the information about the dealer, supplier, manufactured goods and raw materials as it uses MongoDB to store the data on the backend and the frontend is developed using Java on NetBeans to provide a good Graphical User Interface (GUI) so that any person without any technical background can access the inventory. The present work may help in high and agreed level of customer service. It may lead to opt for flexible capacity and enable us to deal with perks and troughs in demand.

In India, there is a strong need of Crime Surveillance system. In the current era, there are many traditional Hotspot analysis techniquesare used but still improvements are needed for getting efficient output as crime hotspot (area with high intensity of crimes). The strongest pillar of a perfect surveillance system is data related to different cognizable crimes. After collecting the data, the next step is transformation of data into meaningful information. This paper aims to throw light on the areas having high intensity of crimes that is spoiling an environment of particular places. Criminal hotspots are detected using statistical analysis tool (SaTScan) and visualize the result with the help of GIS (Google Earth) for pointing the location of district-wise crime hotspots. Hence, the presented work provides an extremely efficient output of criminal hotspots in the state of Haryana which helps to explore the areas for raising the people's awareness regarding the dangerous locations and help police force for using their resources efficiently for the avoidance of criminal activities.