arXiv subjects

Murat Ozer

Publications and source records attributed to Murat Ozer.

At least 37 records · Page 2Linked to original sources

arXiv2024

Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems

In the dynamic urban landscape, where the interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This datasets is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

cs.CV↗

arXiv2024

A Comprehensive Analytical Review on Cybercrime in West Africa

Cybercrime is a growing concern in West Africa due to the increasing use of technology and internet penetration in the region. Legal frameworks are essential for guiding the control of cybercrime. However, the implementation proves challenging for law enforcement agencies due to the absence of a dedicated and effective regional institutional follow-up mechanism. This study conducted a systematic literature review focusing on West Africa's prevalence of cybercrime, governing policies, regulations, and methodologies for combating cybercrime. West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts. This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework and broadening research to cover pivotal areas like cyber aggression and cyberbullying. Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies and policies. Overall, this study provides invaluable insights into the state of cybercrime in West Africa to guide the formulation of potent prevention and intervention strategies.

cs.CY↗

arXiv2024

CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.

cs.HC↗

arXiv2023

Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis

A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure detection; However, simplicity and affordability are key concepts for daily use while preserving the accuracy of the detection. In this study, we propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four seconds of training time; the system was verified and validated using 500 subjects, with seizure detection data sampled at 178 Hz, the operated with a mean accuracy of (94.5%).

cs.LG↗

arXiv2023

A Secure Open-Source Intelligence Framework For Cyberbullying Investigation

Cyberbullying has become a pervasive issue based on the rise of cell phones and internet usage affecting individuals worldwide. This paper proposes an open-source intelligence pipeline using data from Twitter to track keywords relevant to cyberbullying in social media to build dashboards for law enforcement agents. We discuss the prevalence of cyberbullying on social media, factors that compel individuals to indulge in cyberbullying, and the legal implications of cyberbullying in different countries also highlight the lack of direction, resources, training, and support that law enforcement officers face in investigating cyberbullying cases. The proposed interventions for cyberbullying involve collective efforts from various stakeholders, including parents, law enforcement, social media platforms, educational institutions, educators, and researchers. Our research provides a framework for cyberbullying and provides a comprehensive view of the digital landscape for investigators to track and identify cyberbullies, their tactics, and patterns. An OSINT dashboard with real-time monitoring empowers law enforcement to swiftly take action, protect victims, and make significant strides toward creating a safer online environment.

cs.CY↗

arXiv2023

AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.

cs.CV↗

arXiv2023

Exploring the Journey to Drug Overdose: Applying the Journey to Crime Framework to Drug Sales Locations and Overdose Death Locations

Drug overdose is a pressing public health concern in the United States, resulting in a significant number of fatalities each year. In this study, we employ the Journey to Crime (JTC) framework borrowed from the field of environmental criminology to examine the association between drug sales locations and overdose death locations. In this research, our objective is to elucidate the trajectory of overdose victims to overdose locations, aiming to enhance the distribution of overdose services and interventions. To the best of our knowledge, no previous studies have applied the JTC framework to investigate drug overdose deaths. By scrutinizing data obtained from the Hamilton County, OH Coroners, and the Cincinnati Police Department, we endeavor to explore the plausible correlation between overdose deaths and drug sales locations. Our findings underscore the necessity of implementing a comprehensive strategy to curtail overdose deaths. This strategy should encompass various facets, including targeted efforts to reduce the accessibility of illicit drugs, the enhancement of responses to overdose incidents through a collaborative multidisciplinary approach, and the availability of data to inform evidence-based strategies and facilitate outcome evaluation. By shedding light on the relationship between drug sales locations and overdose death locations through the utilization of the JTC framework, this study contributes valuable insights to the field of drug overdose prevention. It emphasizes the significance of adopting multifaceted approaches to address this public health crisis effectively. Ultimately, our research aims to inform the development of evidence-based interventions and policies that can mitigate the occurrence and impact of drug overdoses in our communities.

cs.SI↗

arXiv2023

Cyberbullying in Text Content Detection: An Analytical Review

Technological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.

cs.CY↗

arXiv2023

A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images

Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.

cs.LG↗

arXiv2023

Deep Learning Approach for Early Stage Lung Cancer Detection

Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high accuracy. In addition, it can be a beneficial tool to support radiologists' decisions in predicting and detecting lung cancer and its stage.

eess.IV↗

arXiv2022

Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants

Brain Computer Interface (BCI) has great potential for solving many brain signal analysis limitations, mental disorder resolutions, and restoring missing limb functionality via neural-controlled implants. However, there is no single available, and safe implant for daily life usage exists yet. Most of the proposed implants have several implementation issues, such as infection hazards and heat dissipation, which limits their usability and makes it more challenging to pass regulations and quality control production. The wireless implant does not require a chronic wound in the skull. However, the current complex clustering neuron identification algorithms inside the implant chip consume a lot of power and bandwidth, causing higher heat dissipation issues and draining the implant's battery. The spike sorting is the core unit of an invasive BCI chip, which plays a significant role in power consumption, accuracy, and area. Therefore, in this study, we propose a low-power adaptive simplified VLSI architecture, "Zydeco-Style," for BCI spike sorting that is computationally less complex with higher accuracy that performs up to 93.5% in the worst-case scenario. The architecture uses a low-power Bluetooth Wireless communication module with external IoT medical ICU devices. The proposed architecture was implemented and simulated in Verilog. In addition, we are proposing an implant conceptual design.

cs.AR↗

arXiv2022

Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowadays, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.

eess.AS↗

arXiv2022

A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging

The world is still overwhelmed by the spread of the COVID-19 virus. With over 250 Million infected cases as of November 2021 and affecting 219 countries and territories, the world remains in the pandemic period. Detecting COVID-19 using the deep learning method on CT scan images can play a vital role in assisting medical professionals and decision authorities in controlling the spread of the disease and providing essential support for patients. The convolution neural network is widely used in the field of large-scale image recognition. The current method of RT-PCR to diagnose COVID-19 is time-consuming and universally limited. This research aims to propose a deep learning-based approach to classify COVID-19 pneumonia patients, bacterial pneumonia, viral pneumonia, and healthy (normal cases). This paper used deep transfer learning to classify the data via Inception-ResNet-V2 neural network architecture. The proposed model has been intentionally simplified to reduce the implementation cost so that it can be easily implemented and used in different geographical areas, especially rural and developing regions.

eess.IV↗

arXiv2022

Data Breaches in Healthcare Security Systems

Providing security to Health Information is considered to be the topmost priority when compared to any other field. After the digitalization of the patient's records in the medical field, the healthcare/medical field has become a victim of several internal and external cyberattacks. Data breaches in the healthcare industry have been increasing rapidly. Despite having security standards such as HIPAA (Health Insurance Portability and Accountability Act), data breaches still happen on a daily basis. All various types of data breaches have the same harmful impact on healthcare data, especially on patients' privacy. The main objective of this paper is to analyze why healthcare data breaches occur and what is the impact of these breaches. The paper also presents the possible improvements that can be made in the current standards, such as HIPAA, to increase security in the healthcare field.

cs.CR↗

arXiv2022

Review on Action Recognition for Accident Detection in Smart City Transportation Systems

Action detection and public traffic safety are crucial aspects of a safe community and a better society. Monitoring traffic flows in a smart city using different surveillance cameras can play a significant role in recognizing accidents and alerting first responders. The utilization of action recognition (AR) in computer vision tasks has contributed towards high-precision applications in video surveillance, medical imaging, and digital signal processing. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for a smart city. In this paper, we focused on AR systems that used diverse sources of traffic video capturing, such as static surveillance cameras on traffic intersections, highway monitoring cameras, drone cameras, and dash-cams. Through this review, we identified the primary techniques, taxonomies, and algorithms used in AR for autonomous transportation and accident detection. We also examined data sets utilized in the AR tasks, identifying the main sources of datasets and features of the datasets. This paper provides potential research direction to develop and integrate accident detection systems for autonomous cars and public traffic safety systems by alerting emergency personnel and law enforcement in the event of road accidents to minimize human error in accident reporting and provide a spontaneous response to victims

cs.CV↗

arXiv2022

The Dangerous Combo: Fileless Malware and Cryptojacking

Fileless malware and cryptojacking attacks have appeared independently as the new alarming threats in 2017. After 2020, fileless attacks have been devastating for victim organizations with low-observable characteristics. Also, the amount of unauthorized cryptocurrency mining has increased after 2019. Adversaries have started to merge these two different cyberattacks to gain more invisibility and profit under "Fileless Cryptojacking." This paper aims to provide a literature review in academic papers and industry reports for this new threat. Additionally, we present a new threat hunting-oriented DFIR approach with the best practices derived from field experience as well as the literature. Last, this paper reviews the fundamentals of the fileless threat that can also help ransomware researchers examine similar patterns.

cs.CR↗

arXiv2022

A Rule-Based Model for Victim Prediction

In this paper, we proposed a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.

cs.AI↗

arXiv2022

Early Stage Diabetes Prediction via Extreme Learning Machine

Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe diseases that can lead to patient death. Developing and rural areas suffer the most due to the limited medical providers and financial situations. This paper proposed a novel approach based on an extreme learning machine for diabetes prediction based on a data questionnaire that can early alert the users to seek medical assistance and prevent late diagnoses and severe illness development.

cs.LG↗