Mohsen Ahmadi

Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Hajar Kazemi Naeini et al.

This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures. The results indicate that each model has its own strengths and limitations for detecting different types of cracks. Using the SAM's unique crack detection approach, the image is divided into various parts that identify the location of the crack, making it more effective at detecting longitudinal cracks. On the other hand, the U-Net model can identify positive label pixels to accurately detect the size and location of spalling cracks. By combining both models, more accurate and comprehensive crack detection results can be achieved. The importance of using advanced technologies for crack detection in ensuring the safety and longevity of concrete structures cannot be overstated. This research can have significant implications for civil engineering, as the SAM and U-Net model can be used for a variety of concrete structures, including bridges, buildings, and roads, improving the accuracy and efficiency of crack detection and saving time and resources in maintenance and repair. In conclusion, the SAM and U-Net model presented in this study offer promising solutions for detecting cracks in concrete structures and leveraging the strengths of both models that can lead to more accurate and comprehensive results.

Negin Yazdani Motlagh, Matin Khajavi, Abbas Sharifi et al.

In the digital era, the integration of artificial intelligence (AI) in education has ushered in transformative changes, redefining teaching methodologies, curriculum planning, and student engagement. This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie with a keen focus on the novel ChatGPT. Grounded in a typology that views education through the lenses of system, process, and result, the paper navigates the multifaceted applications of AI. From decentralizing global education and personalizing curriculums to digitally documenting competence-based outcomes, AI stands at the forefront of educational modernization. Highlighting ChatGPT's meteoric rise to one million users in just five days, the study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement. However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity. By juxtaposing the promise and pitfalls of AI in education, this paper advocates for a harmonized synergy between AI tools and the educational community, emphasizing the urgent need for ethical guidelines, pedagogical adaptations, and strategic collaborations.

Mohsen Ahmadi, Masoumeh Farhadi Nia, Sara Asgarian et al.

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance.

Maryam Nikpour, Parisa Behvand Yousefi, Hadi Jafarzadeh et al.

This study confronts the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the necessity for efficient building maintenance, it becomes imperative to explore innovative energy management solutions. We present a comprehensive review of Internet of Things (IoT)-based frameworks aimed at smart city energy management, highlighting the pivotal role of IoT devices in addressing these issues due to their compactness, sensing, measurement, and computing capabilities. Our review methodology encompasses a thorough analysis of existing literature on IoT architectures and frameworks for intelligent energy management applications. We focus on systems that not only collect and store data but also support intelligent analysis for monitoring, controlling, and enhancing system efficiency. Additionally, we examine the potential for these frameworks to serve as platforms for the development of third-party applications, thereby extending their utility and adaptability. The findings from our review indicate that IoT-based frameworks offer significant potential to reduce energy consumption and environmental impact in smart buildings. Through the adoption of intelligent mechanisms and solutions, these frameworks facilitate effective energy management, leading to improved system efficiency and sustainability. Considering these findings, we recommend further exploration and adoption of IoT-based wireless sensing systems in smart buildings as a strategic approach to energy management. Our review underscores the importance of incorporating intelligent analysis and enabling the development of third-party applications within the IoT framework to efficiently meet the evolving energy demands and maintenance challenges

Maryam Taghi Zadeh, Mohsen Ahmadi

The rapid integration of artificial intelligence (AI) into Internet of Things (IoT) and edge computing systems has intensified the need for robust, hardware-rooted trust mechanisms capable of ensuring device authenticity and AI model integrity under strict resource and security constraints. This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems. It systematically examines and compares representative trust anchor mechanisms, including Trusted Platform Module (TPM)-based measurement and attestation, silicon and FPGA-based Physical Unclonable Functions (PUFs), hybrid container-aware hardware roots of trust, and software-only security approaches. The analysis highlights how hardware-rooted solutions generally provide stronger protection against physical tampering and device cloning compared to software-only approaches, particularly in adversarial and physically exposed environments, while hybrid designs extend hardware trust into runtime and containerized environments commonly used in modern edge deployments. By evaluating trade-offs among security strength, scalability, cost, and deployment complexity, the study shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems, whereas software-only trust mechanisms remain insufficient in adversarial and physically exposed settings. The presented comparison aims to clarify current design challenges and guide future development of trustworthy AI-enabled IoT platforms.

Abbas Sharifi, Hajar Kazemi Naeini, Mohsen Ahmadi et al.

Land degradation and air pollution are primarily caused by the salinization of soil and desertification that occurs from the drying of salinity lakes and the release of dust into the atmosphere because of their dried bottom. The complete drying up of a lake has caused a community environmental catastrophe. In this study, we presented an optimization problem to determine the total surface runoff to maintain the level of salinity lake (Urmia Lake). The proposed process has two key stages: identifying the influential factors in determining the lake water level using sensitivity analysis approaches based upon historical data and optimizing the effective variable to stabilize the lake water level under changing design variables. Based upon the Sobol'-Jansen and Morris techniques, the groundwater level and total surface runoff flow are highly effective with nonlinear and interacting impacts of the lake water level. As a result of the sensitivity analysis, we found that it may be possible to effectively manage lake levels by adjusting total surface runoff. We used genetic algorithms, non-linear optimization, and pattern search techniques to solve the optimization problem. Furthermore, the lake level constraint is established based on a pattern as a constant number every month. In order to maintain a consistent pattern of lake levels, it is necessary to increase surface runoff by approximately 8.7 times during filling season. It is necessary to increase this quantity by 33.5 times during the draining season. In the future, the results may serve as a guide for the rehabilitation of the lake.

Masoumeh Farhadi Nia, Mohsen Ahmadi, Elyas Irankhah

Artificial intelligence has dramatically reshaped our interaction with digital technologies, ushering in an era where advancements in AI algorithms and Large Language Models (LLMs) have natural language processing (NLP) systems like ChatGPT. This study delves into the impact of cutting-edge LLMs, notably OpenAI's ChatGPT, on medical diagnostics, with a keen focus on the dental sector. Leveraging publicly accessible datasets, these models augment the diagnostic capabilities of medical professionals, streamline communication between patients and healthcare providers, and enhance the efficiency of clinical procedures. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. This paper sheds light on the current landscape and explores potential future research directions in the burgeoning field of LLMs, offering valuable insights for both practitioners and developers. Furthermore, it critically assesses the broad implications and challenges within various sectors, including academia and healthcare, thus mapping out an overview of AI's role in transforming dental diagnostics for enhanced patient care.

Mohsen Ahmadi, Ali Taghavirashidizadeh, Danial Javaheri et al.

Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federated Learning allows various parties to train a machine learning algorithm on their shared data jointly. This method of collective privacy learning results in the expense of important communication during training. Most large-scale machine-learning applications require decentralized learning based on data sets generated on various devices and places. Such datasets represent an essential obstacle to decentralized learning, as their diverse contexts contribute to significant differences in the delivery of data across devices and locations. Researchers have proposed several ways to achieve data privacy in Federated Learning systems. However, there are still challenges with homogeneous local data. This research approach is to select nodes (users) to share their data in Federated Learning for independent data-based equilibrium to improve accuracy, reduce training time, and increase convergence. Therefore, this research presents a combined Deep-QReinforcement Learning Ensemble based on Spectral Clustering called DQRE-SCnet to choose a subset of devices in each communication round. Based on the results, it has been displayed that it is possible to decrease the number of communication rounds needed in Federated Learning.

Mohsen Ahmadi, Pantea Kiaei, Navid Emamdoost

Mutation analysis is an effective technique to evaluate a test suite adequacy in terms of revealing unforeseen bugs in software. Traditional source- or IR-level mutation analysis is not applicable to the software only available in binary format. This paper proposes a practical binary mutation analysis via binary rewriting, along with a rich set of mutation operators to represent more realistic bugs. We implemented our approach using two state-of-the-art binary rewriting tools and evaluated its effectiveness and scalability by applying them to SPEC CPU benchmarks. Our analysis revealed that the richer mutation operators contribute to generating more diverse mutants, which, compared to previous works leads to a higher mutation score for the test harness. We also conclude that the reassembleable disassembly rewriting yields better scalability in comparison to lifting to an intermediate representation and performing a full translation.

Mohsen Ahmadi, Kevin Leach, Ryan Dougherty et al.

There is a growing body of malware samples that evade automated analysis and detection tools. Malware may measure fingerprints ("artifacts") of the underlying analysis tool or environment and change their behavior when artifacts are detected. While analysis tools can mitigate artifacts to reduce exposure, such concealment is expensive. However, not every sample checks for every type of artifact-analysis efficiency can be improved by mitigating only those artifacts most likely to be used by a sample. Using that insight, we propose MIMOSA, a system that identifies a small set of "covering" tool configurations that collectively defeat most malware samples with increased efficiency. MIMOSA identifies a set of tool configurations that maximize analysis throughput and detection accuracy while minimizing manual effort, enabling scalable automation to analyze stealthy malware. We evaluate our approach against a benchmark of 1535 labeled stealthy malware samples. Our approach increases analysis throughput over state of the art on over 95% of these samples. We also investigate cost-benefit tradeoffs between the fraction of successfully-analyzed samples and computing resources required. MIMOSA provides a practical, tunable method for efficiently deploying analysis resources.

Pantea Kiaei, Cees-Bart Breunesse, Mohsen Ahmadi et al.

Fault injection attacks can cause errors in software for malicious purposes. Oftentimes, vulnerable points of a program are detected after its development. It is therefore critical for the user of the program to be able to apply last-minute security assurance to the executable file without having access to the source code. In this work, we explore two methodologies based on binary rewriting that aid in injecting countermeasures in the binary file. The first approach injects countermeasures by reassembling the disassembly whereas the second approach leverages a full translation to a high-level IR and lowering that back to the target architecture.

Mohsen Ahmadi, Hossein Soleimani

Minutia-based palmprint recognition systems has got lots of interest in last two decades. Due to the large number of minutiae in a palmprint, approximately 1000 minutiae, the matching process is time consuming which makes it unpractical for real time applications. One way to address this issue is aligning all palmprint images to a reference image and bringing them to a same coordinate system. Bringing all palmprint images to a same coordinate system, results in fewer computations during minutia matching. In this paper, using convolutional neural network (CNN) and generalized Hough transform (GHT), we propose a new method to register palmprint images accurately. This method, finds the corresponding rotation and displacement (in both x and y direction) between the palmprint and a reference image. Exact palmprint registration can enhance the speed and the accuracy of matching process. Proposed method is capable of distinguishing between left and right palmprint automatically which helps to speed up the matching process. Furthermore, designed structure of CNN in registration stage, gives us the segmented palmprint image from background which is a pre-processing step for minutia extraction. The proposed registration method followed by minutia-cylinder code (MCC) matching algorithm has been evaluated on the THUPALMLAB database, and the results show the superiority of our algorithm over most of the state-of-the-art algorithms.