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Ajay Singh;Rajesh Kumar Dhanaraj;Seifedine Kadry; 《Expert Systems》2024,41(10):e13642
The swift societal evolution and ceaseless advancement of human value of life have been set forth for reliability as well as rapidity of railway transportation. Latest advances in machine learning approaches as well as surging accessibility of numerous information sources is produced state-of-the-art probabilities for significant, precise train delay identification. In this method called, Barzilai Borwein Incremental Grey Polynomial Regression (BBI-GPR) is introduced for predicting train arrival/departure delays, which utilized for later delay management in an accurate manner with this method comprised into three sections such as, pre-processing, feature selection and classification. First, with the raw ETA train delay dataset as input, Barzilai–Borwein Feature Rescaling-based Pre-processing is applied to model computationally efficient feature rescaled and normalized values. Second with processed features as input, Incremental Maximum Relevance Minimum Redundant-based Feature Selection is applied to select error minimized optimal features. Finally, with optimal features selected as input, Grey Polynomial Regression-based Prediction algorithm is employed to analyse train delay. For confirming proposed BBI-GPR, as well as analyse its performance, compare standard train delay prediction method with existing machine learning-based regression method. Results show that new variants outperform existing train delay prediction method by minimizing train delay prediction time, error rate by 25% and 27% respectively, with improved accuracy rate of 7%, therefore paving ways for efficient train delay prediction. 相似文献
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Javaria Amin Muhammad Sharif Muhammad Almas Anjum Ayesha Siddiqa Seifedine Kadry Yunyoung Nam Mudassar Raza 《计算机、材料和连续体(英文)》2021,69(1):785-799
White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness. 相似文献
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R. Bhaskaran S. Saravanan M. Kavitha C. Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash 《计算机系统科学与工程》2023,44(1):235-247
Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques. 相似文献
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An experimental facility is designed and manufactured to measure the solar flux density distribution on a central flat receiver due to a single flat heliostat. The tracking mechanism of the heliostat is controlled by two stepping motors, one for tilt angle control and the other for azimuth angle control. A x-y traversing mechanism is also designed and mounted on a vertical central receiver plane, where the solar flux density is to be measured. A miniature solar sensor is mounted on the platform of the traversing mechanism, where it is used to measure the solar flux density distribution on the receiver surface. The sensor is connected to a data acquisition card in a host computer. The two stepping motors of the heliostat tracking mechanism and the two stepping motors of the traversing mechanism are all connected to a controller card in the same host computer. A software “TOWER” is prepared to let the heliostat track the sun, move the platform of the traversing mechanism to the points of a preselected grid, and to measure the solar flux density distribution on the receiver plane. Measurements are carried out using rectangular flat mirrors of different dimensions at several distances from the central receiver. Two types of images were identified on the receiver plane—namely, apparent (or visible) and mirror-reflected radiation images. Comparison between measurements and a mathematical model validates the mathematical model. 相似文献
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Steinberg EB Henderson A Karpati A Hoekstra M Marano N Souza JM Simons M Kruger K Giroux J Rogers HS Hoffman MK Kadry AR Griffin PM;Burrito Working Group 《Journal of food protection》2006,69(7):1690-1698
From October 1997 through March 1998, three outbreaks of gastrointestinal illness among school children were linked to company A burritos. In September 1998, a similar outbreak occurred in three North Dakota schools following lunches that included company B burritos. We conducted an investigation to determine the source of the North Dakota outbreak, identify other similar outbreaks, characterize the illness, and gather evidence about the cause. The investigation included epidemiologic analyses, environmental investigation, and laboratory analyses. In North Dakota, a case was defined as nausea, headache, abdominal cramps, vomiting, or diarrhea after lunch on 16 September 1998. Case definitions varied in the other states. In North Dakota, 504 students and staff met the case definition; predominant symptoms were nausea (72%), headache (68%), abdominal cramps (54%), vomiting (24%), and diarrhea (16%). The median incubation period was 35 min and median duration of illness was 6 h. Eating burritos was significantly associated with illness (odds ratio, 2.6; 95% confidence interval, 1.6 to 4.2). We identified 16 outbreaks that occurred in seven states from October 1997 through October 1998, affecting more than 1,900 people who ate burritos from two unrelated companies. All tortillas were made with wheat flour, but the fillings differed, suggesting that tortillas contained the etiologic agent. Results of plant inspections, tracebacks, and laboratory investigations were unrevealing. More than two million pounds of burritos were recalled or held from distribution. The short incubation period, symptoms, and laboratory data suggest that these outbreaks were caused by an undetected toxin or an agent not previously associated with this clinical syndrome. Mass psychogenic illness is an unlikely explanation because of the large number of sites where outbreaks occurred over a short period, the similarity of symptoms, the common food item, the lack of publicity, and the link to only two companies. A network of laboratories that can rapidly identify known and screen for unknown agents in food is a critical part of protecting the food supply against natural and intentional contamination. 相似文献
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Internet of Things (IoT) is mainly used to connect different embedded objects over the internet to make communication between them possible. With the help of IoT, devices find a way to interact, work together, and study from each other's experiences just like humans do. IoT finds its way in applications such as smart home, smart city, healthcare, agriculture, and so on. The name smart home arises due to the automation of the normal home appliances to make it smart. When the devices of the normal smart home are connected via the internet, they become a part of the IoT. The smart home should ensure the following characteristics such as security, comfort, convenience, and energy saving. The article presents a technique for IoT controlled devices in a smart home using context-based fuzzy logic. Fuzzy logic is mainly used to monitor and analyze the real-time data collected from the sensors in the smart homes from various environments. Context-based fuzzy logic uses a multivalued logic principle which differs from the normal Boolean logic, where the truth value lies between only zero and one (ie, true or false). The proposed smart home is implemented in a real case scenario where it yields an accuracy of 90.5%, response time of 6.41 milliseconds, and an F-measure of 97%. 相似文献
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K. Kalyani Sara A Althubiti Mohammed Altaf Ahmed E. Laxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam 《计算机、材料和连续体(英文)》2023,75(1):149-164
Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset. 相似文献
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Ramya Nemani G. Jose Moses Fayadh Alenezi K. Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han 《计算机系统科学与工程》2023,47(1):919-935
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning (SDMIRP-SMODL) model. In the presented SDMIRP-SMODL model, the feature subset selection process is performed by the SMO algorithm, which in turn minimizes the computation complexity. For rainfall prediction. Convolution neural network with long short-term memory (CNN-LSTM) technique is exploited. At last, this study involves the pelican optimization algorithm (POA) as a hyperparameter optimizer. The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class. The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques. 相似文献