Rising Crime Rates Research Articles

Assessing Islamic Moral Values Among Muslim Youth in Nigeria

Any society’s stability hinges upon its members’ collective adherence to moral principles and practices. Morality, universally relevant, acts as a unifying force that transcends religious, racial, and regional boundaries. This study examines the moral values of Muslim youth in Nigeria, highlighting the rising deviation from Islamic principles towards Western culture and immorality. Through a qualitative research strategy, data was collected by conducting interviews with selected youths across Nigeria and experts in Islamic moral teachings. Thematic analysis is applied to discern crucial patterns in the study. The study reveals that forsaking Islamic moral values and adopting Western culture results in moral decadence, which contributes to rising crime rates, including sexual assaults, substance abuse, highway robbery, educational setbacks, and mental health disorders. The root causes of the deterioration include the lack of individual morality, societal moral ethics, and ignorance of Islamic moral values. The study recommends integrating Islamic moral teachings from the Qur’an and Sunnah alongside encouraging positive cultural and moral behaviours to mitigate these challenges. Furthermore, the study emphasizes the importance of raising awareness among youth regarding the detrimental effects of Western and non-Islamic culture. Tackling the shortcomings in personal and societal morality, addressing moral ignorance, and engaging parents, schools, and the community are suggested as essential measures to mitigate the impact of moral decline.

IoT Sensor-Based Convolutional Neural Network System for Concealed Weapon Detector for Security Enhancement

Security has been a major concern in our societies due to the rise in crime rate, most especially in a crowded area. Concealed weapons have been posing a significant threat to government, law enforcement, security agencies, and civilians. Existing weapons detection systems seem to be not culpable of detecting concealed weapons without the cooperation of the person being searched. There remains a need for a weapons detector that can detect and identify concealed weapons for security enhancement in Nigeria. For this purpose, computer vision methods and a deep learning approach were applied for the identification of a weapon from captured images downloaded from the internet as a prototype for the study. Recent work in deep learning and machine learning using convolutional neural networks has shown considerable progress in the areas of object detection and recognition. The CNN algorithms are trained on the collected datasets to identify and differentiate between weapons and non-weapons. We built a concealed weapon detection system prototype and conducted a series of experiments to test the system's accuracy, precision, and false positives. The models were compared by evaluating their average values of sensitivity, specificity, F1 score, accuracy, and the area under the receiver operating characteristic curve (AUC). The experimental findings clearly demonstrated that the ResNet-50 model performed better than the VGG-16 and Alex Net models in terms of sensitivity, specificity, and accuracy.

TRENDS AND PATTERNS OF DRUG ABUSE

This study investigates the trends and patterns of drug abuse and its associated criminal behaviors in Yola Metropolis, Adamawa State. The primary objective is to assess the prevalence and patterns of illicit drug use, exploring its correlation with rising crime rates in the metropolis. Utilizing a quantitative research design, the study collected data from the National Drug Law Enforcement Agency (NDLEA) and the Nigeria Police Force (NPF) over five years, focusing on reported drug-related offenses and crime incidents. The results revealed a concerning upward trend in drug abuse, particularly in substances like marijuana, tramadol, and methamphetamine, alongside a parallel increase in various criminal activities including theft, assault, and drug trafficking. A significant correlation (Pearson coefficient of approximately 0.999) was found between drug abuse prevalence and crime rates, highlighting the complex relationship between substance abuse and criminality in Yola Metropolis. The findings emphasize the urgent need for integrated public health and law enforcement strategies to address the intertwined issues of drug abuse and crime, including enhanced prevention and treatment programs tailored to the local context. This study contributes to the understanding of drug-related challenges in urban Nigeria, emphasizing the necessity for comprehensive intervention approaches to mitigate the social and public health effects of illicit drug use.

The evolution of affluent support for redistribution in Germany in the context of rising inequalities

This paper explores the changing attitudes of the most affluent individuals towards state intervention in redistribution in Germany in the context of growing inequality. Contrary to the predictions from traditional political economy models, a growing body of research suggests that the most affluent individuals could support more redistributive policies in reaction to higher income inequality due to altruism, fear of externalities (such as rising crime rates), and potential economic and social costs associated with downward mobility. Using survey data from the European Social Survey (ESS) from 2002 to 2020, our results provide robust evidence of our main prediction: we find a significant relative increase in support for redistribution among the top 30% of earners (compared to the bottom 70% of earners). Our heterogeneous analysis then reveals that the substantial increase in support for redistribution among the affluent has increased among supporters of the political left. This trend seems, however, to be comparatively less pronounced in East Germany.

Change in Depression Severity Among Depressed Older Crime Victims.

The crime rates in urban centers are rising. The psychological impact of crime can range from distress to longstanding mental health impairment. Despite the rising crime rates and psychological impact on victims, little research has been conducted on older adult crime victims' mental health. In this manuscript we describe the profile of older adult crime victims in New York City referred for mental health services, their rates of depression, and the preliminary outcome data from a brief therapy intervention (PROTECT) targeting depression among victims. Building on prior research demonstrating the efficacy of PROTECT for depressive symptom reduction in elder abuse victims, the present study hypothesized elder crime victims who completed PROTECT therapy would demonstrate clinically meaningful depression symptom reduction as measured by the Patient Health Questionnaire-9 (PHQ-9). Additionally, it was hypothesized that these clinically significant depression symptom reductions would be demonstrated regardless of sex, gender, victimization type, victimization frequency, or living status. The results demonstrate that 67.7% (21/31) of participants had a clinically significant reduction in PHQ-9 scores (≥5 points) upon completion of PROTECT. There were no significant group differences in clinically significant depression symptom reduction, suggesting that PROTECT is an effective intervention for a diverse sample of elder crime victims. Future research should focus on the mental health impact of crime against older adults and explore reductions in anxiety and PTSD symptoms among crime victims. Through our partnerships we aim to bridge the divide between victim service providers, and mental health service providers to assist victims to recover, heal, and thrive.

Mathematical Approaches to Crime Data Analysis: Enhancing Evaluation and Prevention Strategies

Crime data analysis is crucial for assessment and prevention. This research study examines mathematical techniques to crime data analysis to find effective ways to identify crime trends and design effective preventative methods. The report opens with an overview of the situation, noting rising crime rates and the need for new analysis. Crime data analysis's historical backdrop, mathematical and statistical methods, technical advances, and theoretical frameworks are covered in a thorough literature review. The study covers data collection, preprocessing, analytical technique selection, model assessment, and validation. Crime data interpretation is tested using predictive modelling, geographical analysis, clustering, classification, and network analysis. Case studies show successful implementations and compare methods. The discussion part discusses the results, crime prevention techniques, and limits and obstacles. Future research suggestions are given. This study summarizes major themes, policy consequences, and future prospects, adding to crime data analytic expertise. This study uses mathematical methods to help law enforcement and policymakers improve crime prevention. Keywords: - Crime Data Analysis, Mathematical Approaches, Predictive Modelling, Spatial Analysis, Crime Prevention Strategies

Design And Development of Sketch Based Image Retrieval Using Deep Learning

In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.

Volatile-compound fingerprinting and discrimination of positional isomers in stamp-pad ink tracing using HS-GC-IMS combined with multivariate statistical analysis.

The rising crime rate associated with document forgery has a significant impact on public safety and social stability. In document fraud cases, determining the origin of a particular stamp-pad ink is the most important objective. In this study, a comprehensive analysis of the volatile compounds in quick-drying stamp-pad inks from six commonly used brands were performed for the first time, utilizing a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and multivariate statistical analysis methods. Visual and comparative analysis of the differential volatile components among different stamp-pad ink samples was conducted using fingerprints and volcano plots. A total of 127 volatile compounds were accurately identified, with ketones, esters, alcohols, and aldehydes being the most abundant compounds in the stamp-pad inks. Hierarchical clustering analysis (HCA), including dendrograms and clustering heatmaps, was utilized to explore the correlations between these compounds and the samples. Additionally, the precise identification of positional isomers and functional group isomers of aliphatic compounds was achieved. To achieve accurate discrimination of various stamp-pad ink samples, a multivariate statistical analysis method was utilized to establish a classification model for them. Based on the results obtained from HS-GC-IMS, effective discrimination among different brands of stamp-pad ink samples was achieved through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The model exhibited excellent performance, with the fit index of dependent variables (R2Y) and the predictive index of the model (Q2) values of 0.99 and 0.984, respectively. These results provided significant theoretical evidence for the application of HS-GC-IMS as an efficient technique in the analysis of volatile compounds, identification of positional isomers and functional group isomers, as well as tracing the origin of stamp-pad ink and analyzing the formation time of documents.

Person Re-Identification Using Machine Learning

Abstract: The goal of this research is to develop a system that can spot real-world anomalies in surveillance footage, like burglaries and assaults. Although different methods for anomaly detection have been explored in prior research, the significance of locality within the frame has not been fully explored. Given the rise in crime rates in many nations, accurate identification of people is essential for deterring and solving crimes. However, storing and processing information about suspects using conventional paper-based systems can be labor-intensive and time- consuming. This illustrates the need for more effective techniques for identifying dead bodies after a crime, which can be done using machine learning and image processing methods. Instead of using whole-frame video segments, this study explores the use of spatiotemporal tubes and presents a novel dataset for crime scene detection with bounding box supervision in both the train and test sets. The experiments show that a network trained with spatiotemporal tubes outperforms a model trained with whole-frame videos, with the locality remaining reliable even when extraction errors occur. Additionally, the network can expand the spatiotemporal crime scene dataset without the need for additional human labelling by producing spatiotemporal proposals for fresh surveillance videos using only video-level labels.

The Ever-Shifting Ground of Pretrial Detention Reform

In the past six decades, pretrial detention systems have undergone waves of reform. Despite these efforts, pretrial jail populations across the country continue to swell. The causes of such growth in jail populations are difficult to pinpoint, but some are more readily apparent: Fear over rising crime rates, judicial reluctance to release accused persons, and monetary burdens associated with release have all contributed to increased detention pretrial across criminal legal systems in the United States. This article examines various pretrial detention reform efforts and highlights the need for greater research in the area.

Can mixed land use promote social integration? Multiple mediator analysis based on spatiotemporal big data in Beijing

Social isolation has caused several problems, such as increasing income disparities, aggravated mental problems, and rising crime rate; therefore, various methods have been explored to overcome social isolation. Among them, mixed land use is a significant concern of many researchers. Many previous studies have focused on the impact of isolated land use on social isolation; however, few studies profoundly explore the connection between mixed land use and social integration. Therefore, this study constructed a three-dimensional impact path framework of "attraction mobility tolerance" based on the succession theory and explored the impact of mixed land use on social integration within the Sixth Ring Road area of Beijing based on the Location-based Service Data (LBS) from 2017 to 2020. We determined that mixed land use will influence social integration through the simultaneous mediation of functional attraction, spatial mobility, and species tolerance. The impact of mixed land use on social integration is not always positive, and there are deviations in the paths between the two systems. Therefore, the mixed land use policy should pay more attention to those paths with inconsistent objectives, reverse the contradictory path, and enhance the path-transmit impact to promote social integration.

Municipal bankruptcies and crime

While the importance of municipal bonds for the provision of public services is well-documented, the consequences of municipal bankruptcies remain understudied. We contribute to this literature by studying the effects of municipal bankruptcies on crime. Using the staggered difference-in-differences approach and agency-level crime data, we find that violent and property crime rates rise after successful (approved by the bankruptcy courts) Chapter 9 bankruptcy filings. The association between successful municipal bankruptcy and crime remains robust to alternative estimation methods, including a novel empirical technique that allows for the treatment effect heterogeneity and dynamics, and several robustness checks and falsification exercises. Our exploratory evidence suggests that the rise in crime rates is due to lower public safety expenditures. Our findings highlight the importance of efficient financial management for local government entities and the need for particular attention to law and order in the local jurisdiction going through bankruptcy.

SULTAN'S WISDOM IN PRESERVING CULTURE AS SUSTAINABLE TOURISM DEVELOPMENT

From the perspectives of economic, social, politics, and cultural preservation, the sultan's wisdom can have an impact on the entire ecosystem in the implementation of policies toward sustainable tourism destinations in the Jeron Beteng Region - Yogyakarta. The Jeron Beteng neighborhood, which is inside the palace fortress, is currently dealing with global phenomena like population growth, declining public service quality, decreasing availability of residential areas, traffic congestion, a lack of parking, rising levels of energy consumption, garbage buildup, and rising crime rates. As a result, the Jeron Beteng region can continue to be sustainable thanks to palace culture, which has been described by the sultan's knowledge in formulating policies, along with standards and principles for managing the quality of life.The Yogyakarta Palace, which is the de facto owner and authority of the land and the culture inside it, as well as other supporting papers are used in the research's descriptive qualitative methodologies to gather both primary and secondary data from government officials, community members, and other sources. The methods of gathering data include interviews, observation, and documentation. The study's findings demonstrate the sultan's wisdom toward environmentally friendly tourist attractions in Yogyakarta's Jeron Beteng district, which is deeply concerned with cultural preservation. However, the sultan's position is still crucial in encouraging people to adhere to legal requirements.

Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention.

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then modeled as a time-series classification problem. The proposed method was tested on the popular UCF Crime dataset, and benchmarked against the current state-of-the-art robust temporal feature magnitude (RTFM) method, which relies on the Inflated 3D ConvNet (I3D) preprocessing method. Our results demonstrate an impressive 8.45-fold increase in detection inference speed compared to the state-of-the-art RTFM, along with an F1 score of 92%,outperforming RTFM by 3%. Furthermore, our method achieved these results without requiring expensive data augmentation or image feature extraction.

Weapon Detection System

Abstract: Security is always a main concern in every sphere, due to a rise in crime rate in a crowded event or suspicious lonely areas. Anomalydetection and observance have major applications of computer vision to gear various problems. Due to demand in the protection of safety, security of private properties placement of surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence observance. Detection of weapon and militant using convolution neural network (CNN). Proposed implementation uses two types of datasets. One dataset contains pre-labelled images. And the other one labelled manually contains a set of images. Results are tabulated, both algorithms achieve good efficiency, but their operation in real situations can be based on the trade-off between speed and efficiency. Crime is defined as an act dangerous not only to the person involved, but also to the community as a whole. It is to predict the crime using image dataset and finally calculate accurate performance of the detector. The propose algorithms that are able to alert the human operator when a weapon and militant is visible in the image. It is mainly focusedon limiting the number of false alarms in order to allow for real life application of the system. For future work, it is planned to use in live applicationand to improve the detection and reduce the crime.

Who does Australia Lock Up? The Social Determinants of Justice

Crime rates are generally decreasing and governments in Australia (as elsewhere) have committed to reducing recidivism. However, incarceration rates of certain groups continue to rise, including Indigenous and racialised peoples, those experiencing poverty, mental health issues, addiction, homelessness and people with cognitive disability. A large proportion are in custody for minor offences and/or not yet sentenced; however, political leaders have continued to defend their detention on the grounds of risk to community safety. The sudden drop in people incarcerated during the COVID-19 pandemic, without a commensurate rise in crime rates, highlighted the degree to which incarceration rates are a matter of policy decisions. For a time, public health priorities dominated criminal legal policies. Evidence on the social determinants of health that people experiencing social, economic, political and environmental disadvantage are more likely to experience poorer health outcomes has led to acceptance globally that public health policies must address systemic factors and not just focus on individual behaviour. In this article, we propose that a conceptual framework of the social determinants of justice could valuably inform efforts to reduce the criminalisation and incarceration of targeted and disadvantaged groups.

Farmers’ suicides as an indicator of the agrarian transformation in rural India

The article is a review of the book by the Associate Professor of the Center for Political Studies of the Institute of Social Sciences of the Jawaharlal Nehru University (Delhi, India) Sudhir Kumar Sutar Dilapidation of the Rural: Development, Politics and Farmer Suicides in India (Palgrave Macmillan, 2022). The book considers the problems which the Indian peasants face under the state-driven transition from traditional rural community to agrarian capitalism. The book combines philosophical, sociological and political analysis of the causes and factors of the high number of suicides among medium-sized and small Indian farmers in recent decades. Suicides are the most striking manifestation of the social consequences of the capitalist development in the traditional communal peasant village. Among other consequences, the author considers rural social differentiation, growth of individualism and disunity, destruction of community institutions of support and social control, political protests up to riots, alcoholism, drug addiction, and rising crime rate. Thus, the inevitable economic reforms require compensatory measures of social policy.

Iot Based Home Security System Using Arduino

As home security has become an important aspect in life and is necessary to be in every home due to the rising crime rates and robbery that is happening in out day to day life. In this work in progress paper we evaluate an application of home security system using Arduino. This project is based on the implementation in the IOT domain home security system which will allow to make homes secure from anywhere in the world. The main objective of this project is it should be cost effective and at the same time also secure, and this can be achieved by the use of IOT devices and other low cost electronic components and using them in such a way to build a secure home security system. This project was remove some of the limitations in the previous home security system applications. Home security is very useful application of the IOT domain and we will be using it to build a low cost security system that can be affordable by almost everyone.

Character Education between The Western Context and Islamic Perpective

Character education is essential in Indonesia. The rising crime rate and other instances of moral failings are warning signs that the bulk of people's character is in a dangerous state. According to the constitution, the goal of national education is to produce people who are noble in character as well as skillful and clever. The character education policy guidelines, which comprise 18 characters that excel, mandate this (religious, honest, disciplined, tolerant, and so on). Character education and Akhlak are comparable concepts from the western context and an Islamic perspective. Islam's principal sources for moral instruction are the Qur'an and Hadith, with the Prophet Muhammad SAW serving as an example. The suggested approach to character education in Islam is based on a mental-spiritual approach through soul management, habituation, an exemplary lifestyle, and a healthy environment that involves three different educational institutions: schools, families, and communities.

Impact of Climate Change on Individual and Community Mental Health

This paper provides an overview of the current and prospective climate change-related risks and impacts on individual and community mental health, as outlined by studies from the United States, Canada, the United Kingdom, Australia, Mexico, and Pakistan, in addition to a few European countries from 2000 to January 2022. It argues three major points, first, certain vulnerabilities exist with regards to which populations are most atrisk of experiencing poor psychological well-being. The main vulnerabilities and risk factors highlighted in the paper are low socioeconomic background, young age, and communities having close cultural and working relationships with the environment. Second, climate change-induced natural disasters such as floods, hurricanes, wildfires, and heatwaves can have several impacts on mental health, mainly due to worsening physical health, disruption of community cohesion, and forced relocation. The concept of community resilience is also discussed. Finally, the relationship between heat waves and increased psychological fatigue and feelings of hostility is also explored, linked with rising crime rate which can further impact individual and community mental health. It was concluded that climate change impacts individual and community mental health in many ways and that certain gaps in knowledge, such as the factors influencing the severity of this impact and the reasons behind the existence of vulnerabilities among populations, need to be addressed and incorporated into future action. Moreover, adaptive action needs to be taken in preparing societies for the impact of climate change. This includes increasing accessibility to quality mental healthcare and creating protective legal frameworks for those who are disproportionately affected by interpersonal violence during and after climate-related natural disasters.