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Article: Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network.

Kasgari, Abbas Bagherian / Safavi, Sadaf / Nouri, Mohammadjavad / Hou, Jun / Sarshar, Nazanin Tataei / Ranjbarzadeh, Ramin

Bioengineering (Basel, Switzerland)

2023 Volume 10, Issue 4

Abstract: In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the ...

Abstract	In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern's friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.
Language	English
Publishing date	2023-04-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering10040495
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm.

Ranjbarzadeh, Ramin / Zarbakhsh, Payam / Caputo, Annalina / Tirkolaee, Erfan Babaee / Bendechache, Malika

Computers in biology and medicine

2023 Volume 168, Page(s) 107723

Abstract: Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment ... ...

Abstract	Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Brain Neoplasms/diagnostic imaging ; Algorithms ; Brain ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2023-11-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107723
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Article ; Online: A New Algorithm for Digital Image Encryption Based on Chaos Theory.

Pourasad, Yaghoub / Ranjbarzadeh, Ramin / Mardani, Abbas

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 3

Abstract: In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been ... ...

Abstract	In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme's effectiveness and show that this technique is a suitable choice for actual image encryption.
Language	English
Publishing date	2021-03-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23030341
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Article ; Online: Time-Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring.

Mousavi, Seyed Mortaza / Asgharzadeh-Bonab, Akbar / Ranjbarzadeh, Ramin

Computational intelligence and neuroscience

2021 Volume 2021, Page(s) 8430565

Abstract: One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of ... ...

Abstract	One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner-Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.
MeSH term(s)	Algorithms ; Anesthesia ; Electroencephalography
Language	English
Publishing date	2021-08-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5265
ISSN (online)	1687-5273
ISSN	1687-5265
DOI	10.1155/2021/8430565
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Article ; Online: Presentation of a Segmentation Method for a Diabetic Retinopathy Patient's Fundus Region Detection Using a Convolutional Neural Network.

Valizadeh, Amin / Jafarzadeh Ghoushchi, Saeid / Ranjbarzadeh, Ramin / Pourasad, Yaghoub

Computational intelligence and neuroscience

2021 Volume 2021, Page(s) 7714351

Abstract: Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal ...

Abstract	Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients' fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.
MeSH term(s)	Diabetes Mellitus ; Diabetic Retinopathy ; Fundus Oculi ; Humans ; Neural Networks, Computer ; Retina
Language	English
Publishing date	2021-07-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2021/7714351
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A New Algorithm for Digital Image Encryption Based on Chaos Theory

Yaghoub Pourasad / Ramin Ranjbarzadeh / Abbas Mardani

Entropy, Vol 23, Iss 341, p

2021 Volume 341

Abstract	In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme’s effectiveness and show that this technique is a suitable choice for actual image encryption.
Keywords	digital image encryption ; image processing ; chaos random sequence ; discrete wavelet transform ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	006
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Time-Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring

Seyed Mortaza Mousavi / Akbar Asgharzadeh-Bonab / Ramin Ranjbarzadeh

Computational Intelligence and Neuroscience, Vol

2021 Volume 2021

Abstract	One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner–Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	621
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models.

Jafarzadeh Ghoushchi, Saeid / Memarpour Ghiaci, Ali / Rahnamay Bonab, Shabnam / Ranjbarzadeh, Ramin

Environmental science and pollution research international

2022 Volume 29, Issue 53, Page(s) 79735–79753

Abstract: The idea of the circular economy (CE) has gained prominence in the policies of the European Union (EU), commerce, and academic studies. Basically, CE is capable of achieving the best value and resolving many of the systemic challenges in the society and ... ...

Abstract	The idea of the circular economy (CE) has gained prominence in the policies of the European Union (EU), commerce, and academic studies. Basically, CE is capable of achieving the best value and resolving many of the systemic challenges in the society and commerce of a country, thus leading to sustainable development and preventing irreparable damage to the environment. Medical waste management has proved a daunting challenge with the increase in the global population and the demand for medical services. Fuzzy multi-criteria decision-making approaches try to cover the different and uncertain views of decision-makers (DMs). The present study suggests a novel strategy based on multi-objective optimization using the ratio analysis (MOORA) in the area of spherical fuzzy sets (SFSs) to counterbalance the disadvantages of the failure modes and effects analysis (FMEA) method, such as the lack of weight assignment for risk factors and consideration of uncertainty. In the proposed method, first, the barriers are identified using the FMEA method, and the risk factors are given values. Then, the barriers identified using MOORA are prioritized in the spherical fuzzy (SF) area. The computational procedure of the proposed methodology is established through a case study of the barriers to circular economy implementation in designing sustainable medical waste management systems problems under an SF environment. The proposed approach was compared with IF-MOORA and was found that the results are more reliable using the proposed method, also the ranking in the MOORA method was compared with the TOPSIS method in terms of degree of correlation.
MeSH term(s)	Medical Waste ; Waste Management ; Uncertainty ; Sustainable Development ; Commerce
Chemical Substances	Medical Waste
Language	English
Publishing date	2022-02-07
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-022-19018-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools.

Ranjbarzadeh, Ramin / Caputo, Annalina / Tirkolaee, Erfan Babaee / Jafarzadeh Ghoushchi, Saeid / Bendechache, Malika

Computers in biology and medicine

2022 Volume 152, Page(s) 106405

Abstract: Background: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients ... ...

Abstract	Background: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. Methods: The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. Results: Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. Conclusion: The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.
MeSH term(s)	Humans ; Artificial Intelligence ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Brain Neoplasms/diagnostic imaging ; Brain/pathology
Language	English
Publishing date	2022-12-07
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.106405
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Article ; Online: Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Amin Valizadeh / Saeid Jafarzadeh Ghoushchi / Ramin Ranjbarzadeh / Yaghoub Pourasad

Computational Intelligence and Neuroscience, Vol

2021 Volume 2021

Abstract	Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients’ fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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