Theft Incidents Research Articles

Use of Meat Industry By-products in Pet Food Industry: Prospects and Sustainability for Startups in India

With the increasing nuclear family, urbanization, incidents of theft, and isolation in society in the last decades there has been a drastic rise in the number of companion animals, especially pet dogs in the country. The pet food industry is witnessing rapid growth due to the increasing demand for pet foods since pet owners are much more aware of well-being and a balanced diet. India is one of the emerging pet food markets in Asia-Pacific. The country imports 45-50% of pet food to meet the domestic requirements. Therefore there is a huge opportunity in the Indian Pet Food Industry and immense potential to venture into the pet food segment. With a series of opportunities, the sector also witnessed a few challenges which include the absence of a uniform regulatory framework, isolated government policies, higher taxation for pet food compared to other food processing industries, and scattered availability of raw materials. This article throws light on the prospects, sustainability, regulation issues, scope, and opportunities for start-ups in the pet food manufacturing segment by utilizing meat industry by-products as the primary source of protein components to produce cost-effectively and meet the recommended nutritional standards to fulfill the present demands of pet foods.

Advancements in ATM Security for Movement and Tampering Detection: A Review

Advancements in ATM security have become critical due to rising incidents of theft and vandalism. This review aims to evaluate the current state of movement and tampering detection technologies in ATMs, focusing on the integration of advanced machine learning (ML) and deep learning (DL) techniques. Motivated by the need for robust, real-time security solutions, the review addresses limitations in existing systems, such as inadequate anomaly detection and high false-positive rates. The objective is to synthesize advancements in ML and DL methods, including YOLO-based approaches, to enhance ATM security. By examining various methodologies, this review highlights the strengths and weaknesses of different detection systems and proposes directions for future improvements, particularly through the application of the latest YOLO models.

Unraveling the Hidden Costs: How Cable Theft and Vandalism Fuel Soaring Energy Tariffs in Emerging Economies

Escalating utility costs and challenging financial circumstances make it increasingly difficult for consumers to afford essential services. This financial strain also spills over into the workplace, where employees resort to pilfering company assets, resulting in significant annual losses for these utility firms. Our study aims to identify the organizational factors and individual personality traits that motivate employees to engage in cable theft and vandalism, subsequently driving up tariffs. Utilizing the frameworks of the Reasoned Action Theory (TRA) and the Theory of Planned Behavior (TPB), we seek to elucidate the underlying motivations for such theft and destruction of property. Factors such as an unsatisfactory work environment, perceived unfair treatment by management, subpar wages, and easy opportunities for theft are identified as key motivators. To effectively mitigate these issues, targeted policy measures and strategies need to be implemented to influence employee attitudes and perceptions, thereby reducing incidents of theft and vandalism.

Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.

An Analysis of Livestock Theft in South Africa (2013-2023): Towards a Deeper Understanding of Livestock Dynamics for Enhanced Countermeasures

Livestock theft poses a significant challenge to the agricultural sector in South Africa, impacting farmers’ livelihoods, food security, and economic stability. This article analyses livestock theft in South Africa for the ten-year period from 2013 to 2023. It draws on existing literature, government statistics, and analyses of livestock theft statistics. The study identifies the key factors contributing to livestock theft, including regional disparities, seasonal patterns, and the involvement of organised crime. Key findings reveal a steady decline in reported cases from the 2018/2019 financial reporting year, even though the number of stolen livestock has remained relatively high. This is indicative of underreporting, livestock theft incidents; closed-circuit television (CCTV) and surveillance measures; that have had an influence on the data. Practical recommendations are made for combating livestock theft, including improving reporting mechanisms; conducting targeted research on regional hotspots; implementing interventions during peak theft periods; fostering stakeholder collaboration; and increased investment in the training and capacity-building initiatives for law enforcement agencies. This article contributes to understanding the complexities of livestock theft in South Africa. It offers actionable recommendations for policymakers, law enforcement agencies, and agricultural stakeholders to address this pressing issue and protect the economic livelihoods of farming communities.

A Video Data Analysis of Pet Theft Incidents: An Examination of Offense Form, Situational Dynamics, & Offender Characteristics

ABSTRACT Pet theft is a type of property crime, but pet-owner relationships include emotional dimensions and life experiences that can compound their loss far beyond property value. Their bondedness with humans includes companionship as well as healthy lifestyle and mental wellness benefits, which are abruptly halted at junctures of pet loss. Though pets are stolen for various motives (e.g. ransom, reward, resale, or breeding), this crime form has received little research attention from academic criminology and criminal justice. Toward building a line of inquiry, this paper empirically explores the phenomena of “dognapping” as dogs are the most frequently stolen pet and the only pet for which there is a database large enough for meaningful exploration. Content analysis of online posts and web-based video data (Twitter/X1 posts, YouTube videos, and social media websites dedicated to missing and stolen pets) enabled a delineation of dog theft forms, offense dynamics, and offender characteristics. Findings regarding residential and commercial dog theft center discussion on situational crime prevention implications and additional research opportunities to further evidence and explore this relatively disregarded crime.

Machine Learning Applications in Offense Type and Incidence Prediction

In today's rapidly evolving world, detrimental behaviourhas undeniably emerged as a significant factor leading to the downfall of individuals and communities. The rising prevalence of such behaviourcreates substantial disruptions within a country's population, affecting social stability and economic progress. To mitigate the impact of these harmful actions, it is crucial to identify and address them promptly and effectively. This study evaluates specific patterns of detrimental behaviour using data from Kaggleto predict and analyzeprevalent negative behaviours. Recent incidents of theft, for example, have underscored the importance of understanding the most common types of misconduct, as well as their timing and locations. We can develop targeted strategies to prevent and respond to such incidents by analyzing these patterns. Artificial Intelligence (AI) techniques encompass variouscomputational methods and algorithms designed to enable machines to perform tasks that typically require human intelligence. These techniques are used in various applications, fromnatural language processing to image recognition, and offer powerful tools for behavioral analysis. This project employs advanced AI techniques, such as Naive Bayes, to model and identify patterns in detrimental behavior. Naive Bayes, a probabilistic classifier based on Bayes' theorem, is particularly effective in handling large datasets and making accurate predictions. By applying this algorithm, the study achieves a high level of precision in predicting various types of detrimental behavior, enabling a better understanding of their underlying patterns. This knowledge can inform the development of more effective prevention and intervention strategies, ultimately contributing to the reduction of harmful behaviors and the enhancement of community well-being.

Mobile Tracking System Using SMTP Protocol

Abstract: The objective of this project is to enhance mobile security through a robust Android application capable of tracking, locking, and recovering lost or stolen mobile devices. Utilizing the capabilities of Android devices, the system offers real-time tracking, remote locking, device wiping, and geo fencing features. It supports Android OS and allows users to control their devices remotely via SMS commands or through a web-based dashboard using their account credentials. Users can track the location of their device, remotely lock it, and even manipulate the camera to capture images of the possessor. The rising incidence of mobile device theft, which often results in unauthorized access and personal data breaches, underscores the importance of this sophisticated Mobile Tracking System (MTS). This system addresses these security concerns by providing comprehensive functionalities that enhance user safety and device recovery.

Integration of Sustainability in Risk Management and Operational Excellence through the VIKOR Method Considering Comparisons between Multi-Criteria Decision-Making Methods

In the current context, organizations face an important challenge in managing risks related to environmental, social and governance (ESG) issues. This research presents a general method for prioritizing organizational risks with a focus on sustainability based on the characterization of five typologies of organizational risks and their respective sub-risks, based on an analysis of global reports. Subsequently, paired surveys are administered to a group of experts from various sectors, who assign importance to the organizational sub-risks. Their responses serve as the basis for the prioritization of these risks, using the VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) method, which highlights the following most relevant organizational sub-risks for each type of risk: (1) Lack of ethics in the conduct of business (geopolitical risk); (2) Deficit in economic growth (economic risk); (3) Chemical safety (social risk); (4) Massive data fraud or theft incidents (technological risk); and (5) Water depletion (environmental risk). Additionally, a sensitivity analysis is performed to determine the robustness of the results of the VIKOR method and then compare the correlation coefficients with respect to the results obtained in previous studies for the AHP and ANP methods. Finally, we propose the implementation of a model to manage organizational risks, which are addressed proactively through an integral vision, allowing for continuous improvement and alignment with corporate strategy by means of an operational excellence management system (OEMS).

Analysis of thermal videos for detection of lie during interrogation

The lie-detection tests are traditionally carried out by well-trained experts using polygraph machines. However, it is time-consuming, invasive, and, overall, a cumbersome process, not admissible by the court of law. Trained individuals can easily flaunt these tests. On the other hand, facial thermal imaging could be more effective as it is noninvasive and could be a stealth method of tracking the facial blood flow patterns, which have been proven to detect deceit. This paper presents a method based on facial thermal imaging to detect deception in human subjects. The major issue in such research is the lack of proper real-life databases which emulate the crime scenes. In this paper, first, we have developed a database based on almost real-life theft incidents with due diligence using isolated subjects over a period of time at a government hospital under the plea of free health checkup. The experiment has been conducted at Midnapore Medical College and Hospital, West Bengal, India, with proper ethical committee approval. The participants are selected at the behest of the police department with habitual crime records. Most of them have been repeatedly charged with petty crimes of pick-pocketing and stealing. They are invited individually at different instances of time under the plea of medical checkup where they have been enticed to steal cash. It is followed by a two-stage process, a friendly interaction followed by a slightly tougher interrogation. Their forehead and periorbital region skin surface temperature are recorded by a hidden thermal camera. Upon analysis, conspicuous changes in the temperature profile and blood flow pattern have been observed in the individuals who stole money and those who did not.

Implementation on Anti-Theft Mobile Tracking and Monitoring System

The rapid proliferation of mobile devices has led to a significant increase in the cases of mobile theft and unauthorized access to personal data. To address these security concerns, an “Antitheft Mobile Tracking and Monitoring System” is proposed. The application aims to enhance the security of mobile devices by providing users with the means to track and recover their stolen or lost devices. The Antitheft Mobile Tracking and Monitoring System offers a comprehensive solution to combat mobile theft and enhance device security. By combining real-time GPS tracking, image capturing and use of mail protocols to facilitate them has provided it with capabilities with strong authentication measures, the system provides users with effective tools to mitigate the risks associated with mobile device theft. As mobile devices continue to play an integral role in modern life, such security measures are essential to protect user privacy and personal information. The widespread adoption of mobile devices has revolutionized the way we communicate, work, and access information. With this technological advancement, however, comes an unfortunate increase in the incidents of mobile theft and unauthorized access to personal data. The loss of a mobile device not only represents a financial setback but also exposes users to potential breaches of their privacy and security. This real-time tracking capability enables users and authorized personnel, such as law enforcement agencies, to pinpoint the device's location accurately. Key Words: GPS technology, Real-time Notification, SMTP, Authentication.

Digital micro-banking as a health and protection intervention for street-connected children and youth? Analysis from a Togolese pilot

BackgroundThe SaVa pilot offered street-connected children and youth (C&Y) access to digital micro-banking services to help them save money and thus avoid the violence associated with theft at night. It...

Vehicle Theft Detection and Tracking Based on GSM and GPS

Abstract: The increasing incidence of vehicle theft poses a significant challenge to vehicle owners and law enforcement agencies worldwide. This paper presents a comprehensive solution for addressing this issue through the integration of Global System for Mobile Communications (GSM) and Global Positioning System (GPS) technologies. The proposed system aims to enhance the security of vehicles by providing real-time theft detection and efficient tracking capabilities. The GSM module facilitates communication between the vehicle and a centralized monitoring system, allowing forseamless data transmission. The system leverages the widespread coverage and reliability of GSM networks to ensure that alerts are promptly delivered to relevant stakeholders, irrespective of the vehicle's location. Furthermore, the GPS technology enables real-time tracking of the stolen vehicle, assisting law enforcement agencies in swiftly responding to the incident. The system's tracking feature provides continuous updates on the vehicle's location, allowing for efficient recovery operations. Additionally, the integration of geofencing capabilities allows users to define virtual boundaries, triggering alerts if the vehicle deviates from the predefined geographical area. The proposed system offers a multi-layered security approach, combining GPS-based tracking with GSMenabled theft detection, making it a robust and effective solution for combating vehicle theft. The integration of these technologies not only enhances the chances of recovering stolen vehicles but also acts as a deterrent, discouraging potential thieves. In conclusion, the Vehicle Theft Detection and Tracking System presented in this paper showcases a technologically advanced and practical solution to mitigate the impact of vehicle theft. The integration of GSM and GPS technologies provides a comprehensive and reliable framework that addresses the security concerns associated with vehicle ownership in today's dynamic environment.

Predicting Crime rate in Punjab: An Analysis of Artificial Intelligence based Models

This study aims at measuring and evaluating the accuracy rate or Mean Square Error (MSE) of different machine learning models to predict crime count (dependent variable) on the basis of a number of independent variables such as shift, day, month, police station, district and time of the day. It is a quantitative research study where primary data is collected from rescue 15 call record. The year 2022 has been chosen for the purpose of data under the crime head of theft across the Punjab province which is divided into three eight hourly shifts each in a day during any week of a month. The crime count indicates number of incidents of theft reported during these three shifts per day per month in a district in 2022. The data containing variables is then used for prediction through an appropriate machine learning model. The MSE value of Random Forest Regression (RFR) is greater than MSE value of Multi-Layer Perceptron Regression (MLPR). It is however lower than MSE score of Linear Regression (LR) model and greater than MSE value of Support Vector Regression (SVR). This shows that MLPR model is the most suitable one with least error as compared to other models which have higher MSE values while predicting the dependent variable (theft crime count). The study findings may assist the intelligence branches of police to efficiently utilize their material and human resources while applying the appropriate model of machine learning to predict the number of crime incidents in any geographical dispensation at sub national level.

Integrating Sustainability into Risk Management through Analytical Network Process

Sustainable risk management is becoming widely accepted, making the incorporation of environmental, social, and governance (ESG) issues into strategic planning areas crucial to a responsible business philosophy. This article aims to rank organizational sub-risks with a focus on sustainability, offering a methodology based on the analytical network process (ANP) method to improve decision-making and reduce misrepresentation in qualitative evaluation criteria. An integrated approach is presented, starting with the characterization of five risk typologies based on global reports and then prioritizing risks and sub-risks using the ANP method. The sustainability sub-risks with the highest level of prioritization for each risk typology are (1) massive data fraud or theft incident (technological risk), (2) deficit in economic growth (economic risk), (3) water depletion (environmental risk), (4) lack of ethics in the conduct of business (geopolitical risk), and (5) chemical safety (social risk). Finally, a cosine similarity analysis is developed to compare the results obtained with the results of a risk prioritization performed with the analytical hierarchy process (AHP) method. The differences between the methods generate a similar risk prioritization; the high similarity indicates the consistency of the relationships and the prioritization of the criteria showing convergence. It is essential to mention that the results should be interpreted cautiously, considering the specific context in which this methodology is developed, and we recommend a periodic verification of risks and sub-risks.

Organizational Risk Prioritization Using DEMATEL and AHP towards Sustainability

Risk management represents a challenge for organizations, as it includes environmental, social, and governance (ESG) issues that can negatively impact organizations’ investments. This article shows a general approach for prioritizing organizational risks focused on sustainability, which is applied in a particular case. Based on the analysis of global reports such as the “Global Sustainable Development Report”, “Enterprise Risk Management-Integrating with Strategy and Performance”, and the “Global Risk Report”, five typologies of organizational risks with a focus on sustainability (geopolitical, economic, social, technological, and environmental) that support the concern for sustainability in organizations are characterized, taking into account viability and equitability. Additionally, some sub-risks are proposed for each characterized typology of risk. Subsequently, the application of paired surveys assigned to a group of experts formed by executives from the service sector, auditing and consulting firms, the oil and gas sector, the manufacturing sector, and the financial sector is carried out; the responses obtained are consolidated and used in this study as input for the application of DEMATEL and AHP methods to prioritize risks and sub-risks, respectively. The result obtained via the DEMATEL method is the following risk prioritization: (1) economic, (2) geopolitical, (3) social, (4) technological, and (5) environmental. Using the AHP method, the sustainability sub-risks with the highest level of prioritization for each typology of risk are (1) massive data fraud or theft incident (technological risk), (2) deficit in economic growth (economic risk), (3) water depletion (environmental risk), (4) lack of ethics in the conduct of business (geopolitical risk), and (5) chemical safety (social risk). The sensitivity analysis presents positive and negative values, indicating that the positive results do not generate substantial changes between the characterized sub-risks. On the other hand, the negative results indicate a notable decrease in the relative importance of the sub-risks. It is crucial to highlight that the observed variations remain within realistic limits and reflect the uncertainty inherent in decision-making in a dynamic environment.

Theft and Vandalism Control Measures on Construction Sites

Purpose: The goal of this study is to determine and improve the best security practices within Ghanaian construction sites to control theft and vandalism incidents.
 Design/Methodology/Approach: A purposive sampling technique was adopted for the population which were indigenous small and medium-sized construction firms of D1KI and D2K2 categories to complete the semi-structured questionnaire. Out of the total of 80 questionnaires distributed, only 65 responses were retrieved for analysis. Descriptive statistics comprising, frequencies and mean scores were used. 
 Findings: The findings showed that the most effective and often utilized security measure on construction sites is the hiring of watchmen, with security cameras, alarm systems, and warning signs being the least frequently employed. Sites would most likely be protected from theft by effective implementation of the most important security measures while also guaranteeing that the value for money invested in site security is also realized.
 Research Limitation/Implications: The effectiveness of theft and vandalism control measures may vary based on specific site characteristics, such as location, size, and type of construction. Generalising findings from one site to all construction contexts may not be applicable.
 Social Implication: Incidents of theft and vandalism can lead to social tensions within the community, especially if residents perceive construction activities as a source of risk. Effective control measures mitigate these tensions, fostering a more harmonious relationship between construction companies and the local population.
 Practical Implication: Minimizing the impact of theft and vandalism contributes to the timely completion of construction projects. Unanticipated delays due to security incidents can be avoided, ensuring that projects adhere to planned schedules and deadlines.
 Originality/ Value: The originality of this paper lies in its targeted investigation into theft and vandalism control measures on small and medium-sized construction sites, offering innovative and contextually relevant solutions that address the unique challenges faced by SMEs in the construction industry.

The Effect of Regional Electronic Cargo Tracking Systems on Cargo Safety at the Kenya Revenue Authority

The Regional Electronic Cargo Tracking System (RECTS) was adopted by the Kenya Revenue Authority (KRA) to enhance cargo security and boost revenue collection. RECTS is aimed at addressing inefficiencies in vendor-operated Electronic Cargo Tracking Systems (ECTs) whose geo-fence routes can be manipulated, making it challenging to determine the exact location of the cargo. Their failure to send alerts on seal tampering and diversions has resulted in a substantial loss of revenue. This study sought to assess the impact of RECTS on cargo security with a focus on KRA customs operations. The study addressed the impact of RECTS cargo tracking on cargo security, the impact of RECTS cargo monitoring on cargo security, and the measurement of the impact of RECTS real-time response on cargo security at KRA. The research adopted a descriptive research design. The target population for this study was the 140 Rapid Response Units (RRU) and the Cargo Monitoring Unit (CMU) staff working at the five designated RRU stations along the Northern Corridor. Stratified simple random sampling was used to select 104 KRA staff to participate in the study. The primary data was collected by means of a self-administered questionnaire provided to the staff working at the five designated stations. Secondary data was collected from the records of the Cargo Monitoring Unit on incidents of theft, tampering and damage of cargo. The data was analysed using SPSS to compute descriptive and inferential statistics. The study established that the independent variables of cargo tracking, cargo monitoring and real-time response, significantly influence the dependent variable as they account for an 82.90 per cent variation in cargo security. The results of a multiple regression analysis established that cargo tracking has a positive and significant effect on cargo security (β1=0.873, p=0.000); that cargo monitoring has a positive and significant effect cargo security (β2=0.175, p=0.015); and that real-time response has a positive and significant effect cargo security (β3=0.222, P=0.000). The findings of the study revealed that cargo tracking enhances the security of the cargo by deterring cases of cargo diversion and by providing the exact location of the stolen, damaged or tampered cargo enabling the Rapid Response Unit to locate the cargo and resolve the situation immediately. Cargo monitoring enhances cargo security by providing real-time alerts in respect of cargo seal tampering which minimises the tampering that previously occurred during changeover of seals at border points. Additionally, real-time response enhances the security of cargo by reducing the time taken by the Rapid Response Unit to respond to and resolve cases of theft, tampering and cargo accidents during transit.

Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.

Electric Theft Detection Based on Multilayer Backpropagation Neural Network Optimized by Sine Chaotic Genetic Algorithm

Abstract In the era of big data, the growing volume of data in electrical systems has led to a rise in electric theft incidents, posing challenges to grid security. This paper introduces a detection method using the Sine chaotic genetic algorithm to optimize multilayer Backpropagation (BP) neural networks. Initially, a comprehensive dataset is compiled through extensive data collection. A multilayer BP neural network is then trained on this dataset for automated theft identification. Leveraging the Sine chaotic genetic algorithm further enhances network performance. Experimental results show an 88% prediction accuracy, offering improved accuracy, speed, and usability over traditional methods.