Proposing a new method of image classification based on the AdaBoost deep belief network hybrid method

Image classification has different applications. Up to now, various algorithms have been presented for image classification. Each of these method has its own weaknesses and strengths. Reducing error rate is an issue which much researches have been carried out about it. This research intends to optimize the problem with hybrid methods and deep learning. The hybrid methods were developed to improve the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In fact, this method is anunsupervised method, in which all layers are one-way directed layers except for the last layer. So far, various methods have been proposed for image classification, and the goal of this research project was to use a combination of the AdaBoost method and the deep belief network method to classify images. The other objective was to obtain better results than the previous results. In this project, a combination of the deep belief network and AdaBoost method was used to boost learning and the network potential was enhanced by making the entire network recursive. This method was tested on the MINIST dataset and the results were indicative of a decrease in the error rate with the proposed method as compared to the AdaBoost and deep belief network methods.

A new approach to image classification based on a deep multiclass AdaBoosting ensemble

In recent years, deep learning methods have been developed in order to solve the problems. These methods were effective in solving complex problems. Convolution is one of the learning methods. This method is applied in classifying and processing of images as well. Hybrid methods are another multi-component machine learning method. These methods are categorized into independent and dependent types. Ada-Boosting algorithm is one of these methods. Today, the classification of images has many applications. So far, several algorithms have been presented for binary and multi-class classification. Most of the above-mentioned methods have a high dependence on the data. The present study intends to use a combination of deep learning methods and associated hybrid methods to classify the images. It is presumed that this method is able to reduce the error rate in images classification. The proposed algorithm consists of the Ada-Boosting hybrid method and bi-layer convolutional learning method. The proposed method was analyzed after it was implemented on a multi-class Mnist data set and displayed the result of the error rate reduction. The results of this study indicate that the error rate of the proposed method is less than Ada-Boosting and convolution methods. Also, the network has more stability compared to the other methods

Anomaly detection system based on deep learning for cyber physical systems on sensory and network datasets

Cyber-physical systems (CPSs), a type of computing system integrated with physical devices, are widely used in many areas such as manufacturing, traffic control, and energy. The integration of CPS and networks has expanded the range of cyber threats. Intrusion detection systems (IDSs), use signature based and machine learning based techniques to protect networks, against threats in CPSs. Water purifying plants are among the important CPSs. In this context some research uses a dataset obtained from secure water treatment (SWaT) an operational water treatment testbed. These works usually focus solely on sensory dataset and omit the analysis of network dataset, or they focus on network information and omit sensory data. In this paper we work on both datasets. We have created IDSs using five traditional machine learning techniques, decision tree, support vector machine (SVM), random forest, naïve Bayes, and artificial neural network along with two deep methods, deep neural network, and convolutional neural network. We experimented with IDSs, on three different datasets obtained from SWaT, including network data, sensory data, and Modbus data. The accuracies of proposed methods show higher values on all datasets especially on sensory (99.9%) and Modbus data (95%) and superiority of random forest and deep learning methods compared to others

Helicobacter pylori

Helicobacter pylori (HP) is a common worldwide infection with known gastrointestinal and nongastrointestinal complications. One of the gastrointestinal side effects posed for this organism is its role in diabetes and increased insulin resistance. The aim of this study was to evaluate the association between HP and insulin resistance in type 2 diabetic patients and nondiabetics. This cross-sectional study was carried out from May to December 2013 on 211 diabetic patients referred to diabetes clinic of Shahid Beheshti Hospital of Qom and 218 patients without diabetes. HP was evaluated using serology method and insulin resistance was calculated using HOMA-IR. The prevalence of H. pylori infection was 55.8% and 44.2% in diabetics and nondiabetics (P=0.001). The study population was divided into two HP positive and negative groups. Among nondiabetics, insulin resistance degree was 3.01±2.12 and 2.74±2.18 in HP+ and HP− patients, respectively P=0.704. Oppositely, insulin resistance was significantly higher in diabetic HP+ patients rather than seronegative ones (4.484±2.781 versus 3.160±2.327, P=0.013). In diabetic patients, in addition to higher prevalence of HP, it causes a higher degree of insulin resistance

Insulin resistance and coronary artery disease in non-diabetic patients: Is there any correlation?

Background: Cardiovascular diseases are the most common causes of death in the world and type 2 diabetes is one of them because it is highly prevalent and doubles heart disease risk. Some studies suggest that insulin resistance is associated with coronary artery disease in non-diabetics. The aim of this study was to evaluate the association of insulin resistance (IR) and coronary artery disease (CAD) in non-diabetic patients. Methods: In this cross-sectional study, from September 2014 to July 2015, 120 patients referring to Shahid Beheshti Hospital of Qom were evaluated. Their medical history, baseline laboratory studies, BMI and GFR were recorded. After 8 hours of fasting, blood samples were taken from the patients at 8 am, including fasting glucose and insulin level. We estimated insulin resistance using the homeostatic model assessment index of IR (HOMA-IR). Finally, we evaluated the association between IR and CAD. Results: Totally, 120 patients were assigned to participate in this study, among them, 50 patients without CAD and 70 with coronary artery stenosis. Insulin resistance (HOMA-IR> 2.5) was positive in 59 (49.3%) patients and negative in 61 (50.7%) patients. Hence, the correlation between IR and CAD was not statistically significant (P=0.9). Conclusions: In this study, although the correlation was not found between insulin resistance and coronary heart disease, among men, we found a significant association between coronary heart disease and insulin resistance

Monkeypox detection using deep neural networks

BACKGROUND: In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities.METHODS: Deep Neural Networks (DNNs) have shown promising performance in detecting COVID-19 and predicting its outcomes. As a result, researchers have begun applying similar methods to detect Monkeypox disease. In this study, we utilize a dataset comprising skin images of three diseases: Monkeypox, Chickenpox, Measles, and Normal cases. We develop seven DNN models to identify Monkeypox from these images. Two scenarios of including two classes and four classes are implemented.RESULTS: The results show that our proposed DenseNet201-based architecture has the best performance, with Accuracy = 97.63%, F1-Score = 90.51%, and Area Under Curve (AUC) = 94.27% in two-class scenario; and Accuracy = 95.18%, F1-Score = 89.61%, AUC = 92.06% for four-class scenario. Comparing our study with previous studies with similar scenarios, shows that our proposed model demonstrates superior performance, particularly in terms of the F1-Score metric. For the sake of transparency and explainability, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-Cam) were developed to interpret the results. These techniques aim to provide insights into the decision-making process, thereby increasing the trust of clinicians.CONCLUSION: The DenseNet201 model outperforms the other models in terms of the confusion metrics, regardless of the scenario. One significant accomplishment of this study is the utilization of LIME and Grad-Cam to identify the affected areas and assess their significance in diagnosing diseases based on skin images. By incorporating these techniques, we enhance our understanding of the infected regions and their relevance in distinguishing Monkeypox from other similar diseases. Our proposed model can serve as a valuable auxiliary tool for diagnosing Monkeypox and distinguishing it from other related conditions.</p

Flexible Communication of Agents based on FIPA-ACL

Communication in multi-agent systems is an important subject of the current research. In this paper, the syntax and semantics of a multi-agent programming language, called ECCS, are defined. We focus specially on the communication of agents. The main contribution of this paper is a new and flexible way of communication of agents. We finally work out a well known protocol as an example. Keywords: Multi-agent systems, agent communication languages, FIPA-ACL

Towards Realizing Benefits of Information Technology in Organ Transplant:A Review

Organ transplantation comprises of many phases, processes, and activities and involves multiple stakeholders. Effective management of such a complex and costly medical domain requires an efficient, multifaceted solution. Although, Information Technology (IT) can basically play an important role here, it is not clear how IT potentials have been deployed so far. We systematically reviewed MEDLINE, EMBASE, CINAHL, The Cochrane and IEEE databases and identified 27 publications describing IT application in organ transplantation. Although the IT coverage spans over waiting list management, donor-recipient matching, and inpatient and outpatient medication and lab monitoring practices, the coverage is still patchy and whole process IT support is missing in practice.</p