Md. Faruk Abdullah Al Sohan

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• Master of Science
• Lecturer at American International University-Bangladesh

A global health crisis has resulted from the COVID-19 pandemic due to the SARS-CoV-2 coronavirus. It is critical to evaluate the safety and effectiveness of COVID-19 vaccines due to their rapid production and distribution. The study thoroughly investigates the range of side effects reported after vaccination, from mild symptoms to potentially adver...

This research conducts a comprehensive exploration and comparison of various string pattern matching algorithms, including Knuth-Morris-Pratt (KMP), Aho-Corasick, Finite Automata, Boyer-Moore, and Rabin-Karp. Our primary focus is on evaluating their performance with big data. As the size of the data increases, the need for efficient pattern matchin...

GlobeStick is an innovative tactile interface designed to enhance digital map navigation by offering precise and intuitive control for professionals in fields such as disaster management, geography, and meteorology. The system integrates a joystick, rotary encoder, and haptic feedback to enable smooth navigation and accurate zooming, addressing the...

Urban areas face significant parking challenges that contribute to traffic congestion and waste time for drivers. As urban areas transform into smart cities, the implementation of an effective smart parking system is essential. This research proposes an innovative parking solution designed to enhance the user-friendliness of the parking experience....

In the fiercely competitive landscape of the manufacturing sector, reducing downtime and enhancing maintenance efficiency are essential for sustained operational success. This research presents an IoT-enabled digital twin ecosystem designed to improve maintenance scheduling and minimize downtime in complex smart manufacturing environments. The prop...

The growth of drone and Unmanned Aerial Vehicle (UAV) use in cities brings a new set of challenges to safely manage air traffic in crowded urban skies. This research proposes an innovative IoT-enabled and AI-driven air traffic control system designed specifically to meet the unique demands of urban drone applications such as deliveries, surveillanc...

The growing application of smart home technologies has exposed significant vulnerabilities in cybersecurity. This research proposes a lightweight, AI-powered Threat Detection System (TDS) aimed at improving the security of IoT devices in smart homes by combining Azure Cloud and Google Big-Query for efficient data storage and processing. The propose...

In this era of technology, smart cities are becoming more common, but their growing connectivity exposes them to continuous cybersecurity threats, putting their operations and security at risk. This research focuses on enhancing cybersecurity in smart cities by implementing a real-time threat detection and response system, with key metrics such as...

Wildfires and deforestation are major threats to the Sundarbans ecosystem, driving biodiversity loss and escalating carbon emissions. This research proposes an innovative IoT-driven system for wildfire detection and ecosystem management, integrating Smart EcoHydrant Stations (SEHS), Unmanned Aerial Vehicles (UAVs), and blockchain technology. The pr...

Internet blackouts cause significant disruptions across society, affecting various domains. This research investigates the impact of a 13-day internet blackout in Dhaka, Bangladesh, in 2024, focusing on its economic, academic, and mental health consequences. Also, this study emphasizes the multifaceted societal consequences of internet blackouts an...

TensorFlow Keras Applications Image Classification Weather Phenomena Database Accurate weather forecasting is increasingly crucial as climate change intensifies the unpredictability of weather patterns, posing challenges to traditional forecasting models reliant on human observation or numerical methods. Researchers are working on precise weather f...

This paper presents a deep learning architecture Res-Inception model, that combines InceptionV3 and ResNet50V2 to classify different orange diseases with high accuracy. The images selected for the present study were 1614 in number and composed of four classes, namely, black spot disease, citrus canker, citrus greening, and healthy oranges, and chos...

In the rapidly evolving domain of smart home automation, the demand for intelligent and efficient cleaning solutions is increasing. This paper presents a GSM-integrated autonomous cleaning robot powered by Arduino Uno, featuring ultrasonic and bump sensors for effective navigation and obstacle avoidance. The robot ensures thorough cleaning by intel...

The demand for online education is increasing rapidly, making it important to focus on learner satisfaction and effectiveness. Addressing these factors is essential to meet the evolving expectations of learners in the digital education landscape. This research paper explores the effectiveness of online courses by analyzing various aspects, such as...

Rapid urbanization and the increasing demand for sustainable food production have created significant challenges for Bangladesh, necessitating innovative farming solutions. Vertical farming (VF) offers an environmentally friendly alternative, enabling crop production in vertically stacked layers, often indoors and near urban centers, thereby reduci...

Large Language Models (LLMs) have shown impressive capabilities in many fields, leading to speculation about their potential role in achieving Artificial General Intelligence (AGI. Despite their high accuracy in language processing, this study argues that certain architectural and training limitations can stop LLMS from reaching AGI. We explore the...

This paper thoroughly examines current methodologies and algorithms used to analyze electroencephalography (EEG) data to detect fatigue driving states. First, traditional procedures are examined, uncovering their time-consuming nature and elevated rates of mistakes. Different methods include collecting and preparing EEG data from the forehead. This...

Magnetic Resonance Imaging (MRI) is a critical tool for diagnosing and managing cardiovascular diseases due to its non-invasive, high-resolution capabilities and its ability to provide detailed insights into cardiac structure and function. This review examines recent advancements in MRI-based image classification, emphasizing the integration of mac...

Distributed Denial of Service (DDoS) attacks pose significant challenges to network security, causing disruptions and financial losses worldwide. Traditional mitigation techniques often fall short in adapting to the evolving complexity of these attacks. This research work introduces a novel approach to improving the detection and mitigation of DDoS...

Breast cancer affects women worldwide with high fatality rates. A critical initial phase in the rehabilitation treatment procedure is an accurate and early diagnosis. Due to many unknowns, mammography abnormality detection is a difficult task. By developing tools to help physicians identify and treat breast cancer, machine-learning approaches can d...

The integration of Augmented Reality (AR) and Artificial Intelligence (AI) in e-commerce is revolutionizing user experiences, operational efficiency, and revenue generation. This research presents a comprehensive model leveraging AI for applications such as product visualization, virtual try-ons, personalization, and visual search. Initially, the r...

The research focuses on developing a novel model, AMLChain, to provide insights into blockchain technology and automation. The goal is to help prevent financial crimes in the banking industry and improve anti-money laundering practices. By using the features of blockchain, such as transparency, immutability, and automation, significant advancements...

Solar energy systems suffer from the unstable nature of solar irradiance. This paper proposes a novel approach to predict solar irradiance using a sequential model with a Long Short-Term Memory (LSTM) network. The model uses the mean squared error (MSE) loss function and the Adam optimization algorithm to provide robustness to noise and efficient c...

Solar energy systems suffer from the unstable nature of solar irradiance. This paper proposes a novel approach to predict solar irradiance using a sequential model with a Long Short-Term Memory (LSTM) network. The model uses the mean squared error (MSE) loss function and the Adam optimization algorithm to provide robustness to noise and efficient c...

The integration of machine learning and deep learning technologies has revolutionized solar power production by addressing challenges such as variability and unpredictability. This paper explores the application of Explainable AI (XAI) through the proposed SPXAI model to enhance the efficiency and reliability of solar energy systems. SPXAI collects...

The detection of faults in solar panels is essential for generating increased amounts of renewable green energy. Solar panels degrade over time due to physical damage, dust, or other faults. Numerous studies have been conducted to detect and monitor solar panel faults in real-time. This research examines the deployment of deep learning models for i...

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must empha...

Negative externalities of fossil fuels together with adjuvant features of solar energy is driving the global espousal of solar energy technologies. This article presents a forecasting model for photovoltaic (PV) power generation using real-time data analysis of two solar plants through machine learning time series model (MLTSM). The work focuses on...

Negative externalities of fossil fuels together with adjuvant features of solar energy is driving the global espousal of solar energy technologies. This article presents a forecasting model for photovoltaic (PV) power generation using real-time data analysis of two solar plants through machine learning time series model (MLTSM). The work focuses on...

Fake news threatens public debate and decision-making in a digital age. This comprehensive paper, "Unmasking Deception," methodically covers false news detecting tactics and technology. We summarize a wide range of study results, methods, and technological advances to give a thorough overview of disinformation detection and mitigation. Our research...

This research explores the impact of optimization techniques on the performance of C++ code, focusing on the Fibonacci problem. We investigate the importance of efficient coding practices in achieving optimal solutions, particularly for large datasets. By utilizing techniques like memoization, loop unrolling, and function inlining, we aim to enhanc...

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the ex...

Distributed Denial of Service (DDoS) attacks are an ever-present threat to network security and can make online services hard for users to access. Conventional detection methods often struggle to effectively counter new and sophisticated DDoS attacks. This research article aims to assess the effectiveness of several machine learning methods in dete...

In recent times, the use of artificial intelligence (AI) has been more influential in altering diverse industries, including the field of education. One of the important advancements in the domain of AI-driven education belongs to ChatGPT, a robust language model developed specifically to enhance the flow of natural language conversations. The prim...

Academic evaluations hold substantial importance within global educational establishments, constituting a dependable avenue for gauging students' proficiencies and competencies. However, the widespread occurrence of academic dishonesty presents an extensive hurdle to the credibility and reliability of these appraisal processes. To tackle this issue...

E-learning's worldwide accessibility and flexibility have made it popular. Navigating the realm of student engagement in e-learning environments can be challenging. This is because these platforms are virtual, making it difficult for teachers to assess student engagement. Many methods and technologies have been offered to solve this problem and est...

Credit card fraud transactions have been one of the most difficult issues for banks and other financial institutions in recent years. In such events, billions of dollars are lost by financial institutions and the banking system. Concurrently, user information is not safe for that purpose. To address these issues, this paper proposes an efficient so...

Blockchain has proven a very developed and secured technology. It ensures data integrity with authentic connected nodes. Now-a-days, blockchain with IoT is a great combination for secured and smart end to end product delivery. This observation has motivated the research to develop a conceptual model to provide a secure pharmaceutical product delive...

There is a strong etiological connection between human psychology with the surroundings. A pandemic can create a huge negative impact on humans. This impact can be both physical and mental. With the outbreak of Covid-19 pandemic all the stimulus in life has been stopped. It left an adverse impact on the students, too. All kinds of educational insti...

Introducing Blockchain (BC) within the Internet of Things (IoT) is a great combination for ensuring privacy and security to any kind of data. Blockchain ensures the integrity of the data and the nodes connected to it are authentic. In the automation industry, IoT devices are getting highly popular. The food industry is such an example. But BC requi...