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Methods in Medicine CAM. Retracted: A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications. Comput Math Methods Med 2023; 2023:9826259. [PMID: 38124922 PMCID: PMC10732767 DOI: 10.1155/2023/9826259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/8081276.].
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Malik A, Shabaz M, Asenso E. Machine learning based model for detecting depression during Covid-19 crisis. Sci Afr 2023; 20:e01716. [PMID: 37214195 PMCID: PMC10182866 DOI: 10.1016/j.sciaf.2023.e01716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023] Open
Abstract
Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.
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Affiliation(s)
- Arun Malik
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology Jammu, J&K, India
| | - Evans Asenso
- Department of Agricultural Engineering, School of Engineering Sciences, University of Ghana, Accra, Ghana
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Mann S, Bindal AK, Balyan A, Shukla V, Gupta Z, Tomar V, Miah S. Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification. Biomed Res Int 2022; 2022:6392206. [PMID: 35993044 PMCID: PMC9388317 DOI: 10.1155/2022/6392206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022]
Abstract
Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.
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Affiliation(s)
- Suman Mann
- Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India
| | - Amit Kumar Bindal
- Department of Computer Science & Engineering, MM Engineering College, MMDU, Mullana, Ambala, India
| | - Archana Balyan
- Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India
| | - Vijay Shukla
- Department of Computer Science & Engineering, Greater Noida Institute of Technology, Greater Noida, India
| | - Zatin Gupta
- School of Computing Science & Engineering, Galgotias University, Greater Noida, Gautam Buddh Nagar, Uttar Pradesh, India
| | - Vivek Tomar
- Department of Computer Science & Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India
| | - Shahajan Miah
- Department of EEE, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh
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Praveen S, Tyagi N, Singh B, Karetla GR, Thalor MA, Joshi K, Tsegaye M. PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer. Biomed Res Int 2022; 2022:3618197. [PMID: 36033562 PMCID: PMC9410819 DOI: 10.1155/2022/3618197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/30/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.
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Affiliation(s)
| | - Neha Tyagi
- Department of IT, G.L Bajaj Institute of Technology & Management, Greater Noida, India
| | - Bhagwant Singh
- Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES) Dehradun, Uttrakhand, 248007, India
| | - Girija Rani Karetla
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
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Prasad JR, Athawale SV, Raut R, Patil S, Bhandari SU, Shah MA, Ali F. Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness. Computational Intelligence and Neuroscience 2022; 2022:1-9. [PMID: 35875768 PMCID: PMC9303098 DOI: 10.1155/2022/8657313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/18/2022] [Accepted: 06/25/2022] [Indexed: 11/17/2022]
Abstract
The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm’s shortcomings of low optimization accuracy and instability. It also creates high-accuracy solutions in recognized domains quickly. To retain student individuality, students can be divided into two groups: Human psychological variables are incorporated in the algorithm’s improvement: in the “teaching” step of the original BBO algorithm; the “expectation effect” theory of social psychology is combined: “field-independent” and “field-dependent” cognitive styles. As a consequence, low-weight deep neural networks have been designed in such a manner that they require fewer resources for optimal design while also improving quality. A responsive student update component is also introduced to duplicate the effect of the environment on students’ learning efficiency, increase the method’s global search capabilities, and avoid the problem of falling into a local optimum in the first repetition.
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Kumhar SH, Kapula PR, Kaur H, Krishna RR, Kirmani MM, Athavale VA, Ahmad MW, Khan R. Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach. J FOOD QUALITY 2022; 2022:1-12. [DOI: 10.1155/2022/1519451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrition along with several other factors plays a role in the disease progression. Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. The current research is focused on understanding the extent of the application of machine learning tools in enhancing food management for patients with Alzheimer’s since there is no cure known for the same. A total of 100 patient data have been collected where the patients had AD, VD, and MXD. Their demographic data, dietary intake, Fazekas scores, and Hachinski scores were collected (independent variables) and analysed in IBM SPSS by considering the risk of development of AD, VD, and MXD as dependent variables. The findings showed that age is highly related (
) to the development of these three diseases and other demographics are not prioritized. Discussion of other available journal articles showed that nutritional intake, Fazekas scores, Hachinski scores, and gender are also indicators for predicting these diseases (
). Thus, this study concluded that age, gender, diet consumption, and Fazekas and Hachinski scores are important indicators for differentiating AD from other diseases, and ML can be used to create a custom nutrition plan based on the patient’s diet and stage of disease progression. Lastly, future scopes of ML have been explained in this paper.
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Wang H, Sun G, Zheng K, Li H, Liu J, Bai Y. Privacy protection generalization with adversarial fusion. Math Biosci Eng 2022; 19:7314-7336. [PMID: 35730308 DOI: 10.3934/mbe.2022345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Several biometric privacy-enhancing techniques have been appraised to protect face image privacy. However, a face privacy protection algorithm is usually designed for a specific face recognition algorithm. When the structure or threshold of the face recognition algorithm is fine-tuned, the protection algorithm may be invalid. It will cause the network bloated and make the image distortion target multiple FRAs through the existing technology simultaneously. To address this problem, a fusion technology is developed to cope with the changeable face recognition algorithms via an image perturbation method. The image perturbation is performed by using a GAN-improved algorithm including generator, nozzles and validator, referred to as the Adversarial Fusion algorithm. A nozzle structure is proposed to replace the discriminator. Paralleling multiple face recognition algorithms on the nozzle can improve the compatibility of the generated image. Next, a validator is added to the training network, which takes part in the inverse back coupling of the generator. This component can make the generated graphics have no impact on human vision. Furthermore, the group hunting theory is quoted to make the network stable and up to 4.8 times faster than other models in training. The experimental results show that the Adversarial Fusion algorithm can not only change the image feature distribution by over 42% but also deal with at least 5 commercial face recognition algorithms at the same time.
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Affiliation(s)
- Hao Wang
- Beijing University of Technology, Beijing 100124, China
| | - Guangmin Sun
- Beijing University of Technology, Beijing 100124, China
| | - Kun Zheng
- Beijing University of Technology, Beijing 100124, China
| | - Hui Li
- Beijing University of Technology, Beijing 100124, China
| | - Jie Liu
- Beijing University of Technology, Beijing 100124, China
| | - Yu Bai
- Beijing Friendship Hospital, Beijing 100050, China
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