Yaser Ahangari Nanehkaran

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• Doctor of Engineering
• Faculty Member at Yancheng Teachers University

Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. To address this gap, this study employs a deep lear...

We present the block theory-based secondary toppling stability analysis method (BTSTSA), an advanced and novel method specifically designed to assess secondary toppling failures in slopes. This innovative method comprehensively accounts for various failure mechanisms and computes the factor of safety (F.S) for rock slopes. Grounded in Block theory...

Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they...

This article explores the role of artificial intelligence (AI) in predicting nanomaterial particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on forecasting of nanomaterial-altered soil characteristics and behavior. Encouraging findings from these studies underscore AI's abi...

This study focuses on slope stability analysis, a critical process for understanding the conditions, durability, mass properties, and failure mechanisms of slopes. The research specifically addresses rotational-type failure, the primary instability mechanism affecting earth slopes. Identifying and understanding key factors such as slope height, slo...

Background Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran. Methods The research introduces a rigorous comparative analysis that evaluates the predictive prowess of the...

The accurate determination of rock elasticity modulus is crucial for geomechanical analysis and reliable rock engineering designs. Traditional experimental methods have limitations in estimating elasticity modulus, prompting the adoption of artificial intelligence and data-driven techniques to develop adaptive and accurate predictive models. This s...

Naqadeh Region (NR) is one of the most sensitive regions regarding geo-hazards ‎occurrence in Northwest of Iran. The landslides triggering parameters that ‎identified for the studied region are classified as elevation, aspect, slope angle, ‎lithology, drainage density, distance to river, weathering, land-cover, ‎precipitation, vegetation, distance...

Coastal landslides pose significant threats to critical infrastructure in energy-dependent regions like the Assaluyeh anticline. To safeguard communities and economic stability, it is crucial to address uncertainties in coastal landslide susceptibility assessments. This necessity extends beyond scientific exploration and requires pioneering fuzzy l...

Missing value is one of the main factors that cause dirty data. Without high-quality data, there will be no reliable analysis results and precise decision-making. Therefore, the data warehouse needs to integrate high-quality data consistently. In the power system, the electricity consumption data of some large users cannot be normally collected res...

Landslides are one of the most frequent and devastating natural disasters around the world with intensifying impacts on human lives and the environment. To effectively deal with landslides and their consequences, it is primarily important to demarcate areas susceptible to landslides. This can be done through landslide susceptibility mapping (LSM)....

This article is dedicated to the pursuit of establishing a robust empirical relationship that allows for the estimation of in-situ modulus of deformations (Em and Gm) within sedimentary rock slope masses through the utilization of Qslope values. To achieve this significant objective, an expansive and thorough methodology is employed, encompassing a...

Azarshahr County in the northwest of Iran is predominantly covered by Azarshahr travertine, a prevailing sedimentary rock. This geological composition has led to extensive open-pit mining activities, particularly in the western and southwestern parts of the county. The rock's drillability and resistance to excavation play a pivotal role in determin...

Camellia oil is one of the most healthy edible oils in the world. It has the effects of lowering blood pressure, reducing blood fat, and softening blood vessels. Whereas, the Camellia oleifera plant is easily infected by various pests and diseases in the process of growing, which limits the yield of Camellia oil. Thereupon, seeking an intelligent i...

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code’s requirement for specific characteristics. To overcome this issue, researchers have turn...

Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully p...

Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary...

Rapid urban development and increase in construction have significantly altered the surface coverage of cities, resulting in a rise in impervious surfaces such as roofs, streets, and pavements. These changes act as barriers against rainwater infiltration into the soil, leading to a substantial increase in surface runoff. Managing surface runoff has...

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides,...

Urban water distribution networks are crucial infrastructures for providing essential services to society, but their exorbitant costs and limited water resources make their optimization a critical research area. Optimal management and design of these networks can help to reduce costs and enhance their efficiency while meeting technical, economic, a...

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistica...

Idiopathic toe walking (ITW) is a gait disorder where children's initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed...

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID‑19 pandemic‑infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spreadCOVID‑19. Predicting the environmental impacts of these wastes can b...

Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, res...

Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be ap...

Featured Application The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes. Abstract Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement app...

This book with 216 pages includes 11 chapters [Translated to Persian] from Neural networks in atmospheric remote sensing, c2009 by Prof. Dr. William J Blackwell, and Prof. Dr. Frederick W Chen.

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To...

Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to...

The presented article attempted to analysis rockfalls susceptibility mapping which is considered as one the most important type of the landslides with high frequent occurrence. The machine learning based multiple-layer perceptron (MLP) model was used to provide the pridicive model and hazard risk maps for studied area. This artificial neural networ...

Diverse plant diseases have a major impact on the yield of food crops, and if plant diseases are not recognized in time, they may spread widely and directly cause losses to crop yield. In this work, we studied the deep learning techniques and created a convolutional ensemble network to improve the capability of the model for identifying minute plan...

This study describes a deep zero-shot transfer learning model (DZTLM) for predicting mild cognitive impairment (MCI) in patients with Alzheimer’s disease (AD). The proposed DZTLM combines ResNet and deep subdomain adaptation network (DsAN) blocks with a simple data augmentation and transfer technique, Elastic-Mixup. We test the DZTLM using 3D gray...

Due to the growth of IoT applications, especially health care, the information of patients’ health records using data collection from IoT-connected devices has been considered. Biological data of patients in the health record helps to monitor the patient’s status and identify various diseases. Chronic diseases are a type of silent disease that, if...

Cardiovascular disease is one of the most common diseases in the modern world, which, if diagnosed early, can greatly reduce the damage to the patient. Diagnosis of heart disease requires great care, and in some cases, the process can be disrupted by human error. Machine learning methods, especially data mining, have gained international acceptance...

The partitioning-based k-means clustering is one of the most important clustering algorithms. However, in big data environment, it faces the problems of random selection of initial cluster centers randomly, expensive communication overhead among MapReduce nodes and data skewing in data partitions, and others. To solve these problems, this paper pro...

The problem of bilateral matching of teams and scientific and technical talents is studied in new R&D institutions with different forms of uncertain assessment information. A decision method is proposed based on a combination of grey correlation and cloud model. The method firstly applies interval grey numbers to characterize uncertain assessment s...

Deep convolutional neural networks (DCNNs) have been successfully used in many computer visions task. However, with the increasing complexity of the network and continuous growth of data scale, training a DCNN model suffers from the following three problems: excessive network parameters, insufficient capability of the parameter optimization, and in...

Landslide susceptibility assessment plays a vital role in understanding landslide information in advance and taking preventive as well as control measures. The need to initially specify the number of clusters, difficulty in handling noise and quantifying rainfall data, limits the application of traditional clustering models in landslide susceptibil...

Recognition of handwritten digits is one of the most important and challenging issues in recent decades in the field of computer science. Its cursive nature, the right to left writing styles of words and characters as well as various digits shapes have imposed curiosity among numerous researchers to impose a lot of efforts on the recognition of han...

Recent research has raised interest in applying image classification techniques to automatically identify the commodity label images for the business automation of retail enterprises. These techniques can help enterprises improve their service efficiency and realize digital transformation. In this work, we developed a lightweight attention network...

Stock market prediction is extremely important for investors because knowing the future trend of stock prices will reduce the risk of investing capital for profit. Therefore, seeking an accurate, fast, and effective approach to identify the stock market movement is of great practical significance. This study proposes a novel turning point predictio...

Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed f...

Determining grain-size and grading distribution of riverside sediments is very important for issues related to lateral embankment drift, riverside nourishment, management plans, and riverbank stability. In this regard, experimental procedures such as sieve analysis are used in regular assessments which require special laboratory equipment that are...

Frequent itemset mining (FIM) is a significant data mining technique which is widely adopted in numerous applications for exploring frequent items. With the rapid growth and expansion of datasets, FIM has become an interesting topic for many researchers, which has triggered many innovations of numerous FIM algorithms in the big data environment. Th...

Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-ri...

This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitati...

The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based o...

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy imag...

Rice is one of the most important crops in the world, and most people consume rice as a staple food, especially in Asian countries. Various rice plant diseases have a negative effect on crop yields. If proper detection is not taken, they can spread and lead to a significant decline in agricultural productions. In severe cases, they may even cause n...

Handwriting recognition remains a challenge in the machine vision field, especially in optical character recognition (OCR). The OCR has various applications such as the detection of handwritten Farsi digits and the diagnosis of biomedical science. In expanding and improving quality of the subject, this research focus on the recognition of Farsi Han...

Power load prediction which helps make the optimal decision for energy management is of great significance to the safe, reliable, and economical operation of the power system. It is also a challenging task; however, if every large customer of a special transformer is modeled and forecasted for power load, a huge amount of calculation work is needed...

Plant pests have a negative effect on crop yields. If the various insect pests are not identified and controlled properly, they can spread quickly and cause a significant decline in agricultural production. To overcome the challenges, the convolutional neural network (CNN)-based methods have shown excellent performance as it performs automatic feat...

Due to the complexity of the causes of the sliding mass instabilities, landslide susceptibility and hazard evaluation are difficult, but they can be more carefully considered and regionally evaluated by using new programming technologies to minimize the hazard. This study aims to evaluate the landslide hazard zonation in the Tabriz region, Iran. A...

The aim of this study is to design a novel machine learning model named Agglomerative Hierarchical Clustering algorithm based on Overlapped Interval Divergence distance measure (AHC-OLID), for modelling and assessing landslide susceptibility. The AHC-OLID algorithm is proposed to combat the limitations of many clustering algorithms in modelling and...

The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be...

Food security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural information. In this paper, we propose a novel approach to identify plant diseases. The method is divided into...

Crop diseases have a devastating effect on agricultural production, and serious diseases can lead to harvest failure entirely. Recent developments in deep learning have greatly improved the accuracy of image identification. In this study, we investigated the transfer learning of deep convolutional neural networks and modified the network structure...

Crop disease has a negative impact on food security. If diverse crop diseases are not identified in time, they can spread and influence the quality, quantity, and production of grain. Severe crop diseases can even result in complete failure of the harvest. Recent developments in deep learning, particularly convolutional neural networks (CNNs), have...

Plant diseases can cause significant reductions in both the quality and quantity of agricultural products, and they have a disastrous impact on the safety of food production. In severe cases, plant diseases may even lead to no grain harvest completely. Therefore, seeking fast, automatic, less expensive and accurate methods to detect plant diseases...

Plant diseases have a disastrous impact on the safety of food production, and they can cause a significant reduction in both the quality and quantity of agricultural products. In severe cases, plant diseases may even cause no grain harvest entirely. Thus, the automatic identification and diagnosis of plant diseases are highly desired in the field o...

BACKGROUND As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges for centuries, such as different diseases of rice. The severe rice diseases may lead to no harvest o...

The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between...

The economic operation of power transformers is analyzed in the present paper, which is performed by the clustering analysis method. In order to overcome the disadvantages of the conventional k-means algorithm lacking the stability and accuracy, we propose a novel boost k-means algorithm by optimizing the choice of initial cluster centers, and no a...

As Noncommunicable Diseases (NCDs) are affected or controlled by diverse factors such as age, regionalism, timeliness or seasonality, they are always challenging to be treated accurately, which has impacted on daily life and work of patients. Unfortunately, although a number of researchers have already made some achievements (including clinical or...