Penalized Regression Splines-Based Tests for Comparing Two Time Series with Unequal Lengths

The Spline-based modeling has been an established tool for parametric and nonparametricregression modeling because of its continuous progress on theoretical and computational fronts over the last three decades. This thesis explores the idea of penalized spline modeling and goodness of fit testing in the context of time series testing in the frequency domain approach. The comparison of different time series is an important topic in statistical data analysis and has various applications in scientific research. One approach to identifying similarities or dissimilarities between two stationary processes is to compare the spectral densities of both time series. This thesis examines whether two stationary and independent time series with unequal lengths have the same spectral density. A new test statistic is proposed based on penalized splines regression. It relies on penalized splines estimator of an unspecified smooth function for the log-ratio of two spectral estimates, which are obtained from averaging out of the blocked periodograms for corresponding time series. Under the null hypothesis that two spectral densities are the same, the theoretical asymptotic distribution of the test statistic is derived. Several tests have been proposed in recent years: some of them are computationally intensive, and some lack stable size. Also, some current tests have low powers. So, we examined a relatively computationally fast and consistent test using penalized splines regression which reveals stable empirical type I error and good power properties. Simulation studies show that our proposed test is very comparable to the current test statistics in almost every case. Another advantage of our proposed test statistic is that it is very simple to construct and computationally fast based on a low-rank estimation technique

DIAGNOSTYKA PĘCHERZYCY Z WYKORZYSTANIEM SZTUCZNEJ INTELIGENCJI: PODEJŚCIE OPARTE NA UCZENIU MASZYNOWYM DO AUTOMATYCZNEGO WYKRYWANIA ZMIAN SKÓRNYCH

Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.Pęcherzyca to choroba skóry, która może powodować poważne uszkodzenia ludzkiej skóry. Pęcherzyca może powodować inne problemy,  w tym bolesne plamy i zakażone pęcherze, które mogą skutkować sepsą, utratą masy ciała i łaknienia, co może zagrażać życiu, próchnicą zębów i chorób dziąseł. Wczesne wykrycie pęcherzycy może uchronić przed śmiertelną chorobą. Uczenie maszynowe może zaoferować wysoce efektywne podejście do podejmowania decyzji i precyzyjnego prognozowania. Sektor opieki zdrowotnej doświadcza niezwykłych postępów dzięki wykorzystaniu technik uczenia maszynowego. Dlatego do identyfikacji pęcherzycy za pomocą obrazów zaproponowano techniki oparte na uczeniu maszynowym. Proponowany system wykorzystuje duży zbiór danych zebranych z różnych źródeł internetowych do wykrywania pęcherzycy. W zbiorze danych zastosowano augmentację przy użyciu technik takich jak powiększanie, odwracanie, zmiana jasności, zniekształcenie, zmiana wielkości, wysokość i szerokości, aby zwiększyć zakres i różnorodność zbioru danych oraz poprawić wydajność modelu. Do uczenia i oceny modelu wykorzystano pięć popularnych algorytmów uczenia maszynowego, są to: K-Nearest Neighbor (określany jako KNN), drzewo decyzyjne (DT), regresja logistyczna (LR), las losowy (RF) i konwolucyjną sieć neuronowa (CNN). Uzyskane wyniki wskazują, że model oparty na CNN był lepszy od innych algorytmów, osiągając dokładność na poziomie 93%, podczas gdy LR, KNN, RF i DT osiągnęły dokładność odpowiednio 78%, 70%, 85% i 75%.

As You Are, So Shall You Move Your Head: A System-Level Analysis between Head Movements and Corresponding Traits and Emotions

Identifying physical traits and emotions based on system-sensed physical activities is a challenging problem in the realm of human-computer interaction. Our work contributes in this context by investigating an underlying connection between head movements and corresponding traits and emotions. To do so, we utilize a head movement measuring device called eSense, which gives acceleration and rotation of a head. Here, first, we conduct a thorough study over head movement data collected from 46 persons using eSense while inducing five different emotional states over them in isolation. Our analysis reveals several new head movement based findings, which in turn, leads us to a novel unified solution for identifying different human traits and emotions through exploiting machine learning techniques over head movement data. Our analysis confirms that the proposed solution can result in high accuracy over the collected data. Accordingly, we develop an integrated unified solution for real-time emotion and trait identification using head movement data leveraging outcomes of our analysis.Comment: 9 pages, 7 figures, NSysS 201

Estimating growing stock volume in a Bangladesh forest site using Landsat TM and field-measured data

ABSTRACT Estimation of forest Growing Stock (GS) is important in understanding the ecological dynamics and productive capacity of forests. Instead of the traditional cost-effective and time consuming ground based measurements, satellite images are being increasingly used in estimating many forest parameters including GS. This study estimates forest GS at Khadimnagar national park, Sylhet, Bangladesh using regression relationship of vegetation indices (VIs) of Landsat Thematic Mapper (TM) image with field-measured GS. Among the VIs, NDVI (Normalized Difference Vegetation Index) was found to be the best predictor of forest GS with workable accuracy (r 2 = 0.77, P <0.000), while IRI (Infra-red Index) was the poorest estimator (r 2 = 0.38, P < 0.001). This approach could be operationally used for wider scale estimation of GS in similar forest areas of Bangladesh

Assessment of the irrigation feasibility of low-cost filtered municipal wastewater for red amaranth (Amaranthus tricolor L cv. Surma)

Because of the scarcity of clean water, treated wastewater potentially provides an alternative source for irrigation. In the present experiment, the feasibility of using low-cost filtered municipal wastewater in the irrigation of red amaranth (Amaranthus tricolor L cv. Surma) cultivation was assessed. The collected municipal wastewater from fish markets, hospitals, clinics, sewage, and kitchens of households in Sylhet City, Bangladesh were mixed and filtered with nylon mesh. Six filtration methods were applied using the following materials: sand (T1); sand and wood charcoal consecutively (T2); sand, wood charcoal and rice husks consecutively (T3); sand, wood charcoal, rice husks and sawdust consecutively (T4); sand, wood charcoal, rice husks, sawdust and brick chips consecutively (T5); and sand, wood charcoal, rice husks, sawdust, brick chips and gravel consecutively (T6). The water from ponds and rivers was considered as the control treatment (To). The chemical properties and heavy metals content of the water were determined before and after the low cost filtering, and the effects of the wastewater on seed germination, plant growth and the accumulation rate of heavy metals by plants were assessed. After filtration, the pH, EC and TDS ranged from 5.87 to 9.17, 292 to 691 µS cm−1 and 267 to 729 mg L−1, respectively. The EC and TDS were in an acceptable level for use in irrigation, satisfying the recommendations of the FAO. However, select pH values were unsuitable for irrigation. The metal concentrations decreased after applying each treatment. The reduction of Fe, Mn, Pb, Cu, As and Zn were 73.23%, 92.69%, 45.51%, 69.57%, 75.47% and 95.06%, respectively. When we considered the individual filtering material, the maximum amount of As and Pb was absorbed by sawdust; Cu and Zn by wood charcoal; Mn and Cu by sand and Fe by gravel. Among the six filtration treatments, T5 showed the highest seed germination (67.14%), similar to the control T0 (77.14%). The healthy plants/pot ratio (93.62%) was significantly higher for T5, even higher than the control (85.19%). Additionally, the average plant height for T5 (8.097 in.) was statistically identical to the control (8.633 in.). The average number of leaves for T5 (10) was near to the control (12). Finally, the minimum amount of heavy metals accumulated in the plants of T5, whereas the maximum accumulation rate varied among treatments. The accumulated levels of Fe, Mn, Cu, and Zn were within the safe limit; however, the concentrations of Pb and As exceeded their safe limits. The results showed that the low-cost filtration method potentially allows municipal wastewater to be used in irrigation for agricultural production

The influence of a newly developed refrigeration cycle based workpiece cooling method in milling AISI 304 stainless steel

Machining stainless steel produces a high temperature in the cutting zone, leads to a decrease in the tool's longevity and also adversely affects the surface quality of the workpiece. Conventional cooling techniques are not effective in achieving better surface finish and less tool wear formation. This research introduces a novel refrigeration cycle-based workpiece cooling process to improve the machinability of AISI 304 stainless steel. The influence of this method in cutting zone temperature, surface roughness, tool wear formation and workpiece microstructure were compared with those of dry machining. The experimental results demonstrate that the proposed method provides a better surface finish, reducing roughness by 9 %, lowering the cutting zone temperature by 24 %–60 %, and minimizing wear on cutting tools under specific machining conditions. These findings can offer valuable insights for the manufacturing industry

AMDNet23: Hybrid CNN-LSTM deep learning approach with enhanced preprocessing for age-related macular degeneration (AMD) detection

In the context of a growing global population, an automated framework for disease detection can greatly assist medical professionals in diagnosing ocular diseases. This framework offers advantages such as accurate, stable, and rapid outcomes, thereby enhancing the success rate of early disease detection. The primary objective of this study was to enhance the quality of fundus images using an adaptive contrast enhancement algorithm (CLAHE) and Gamma correction as preprocessing techniques. CLAHE is employed to heighten the local contrast of fundus images, while Gamma correction enhances the intensity of relevant features. This research adopts a deep learning approach that integrates convolutional neural networks (CNNs) and both short-term and long-term memory (LSTM) mechanisms. The purpose of this combination is to automatically detect aged macular degeneration (AMD) in fundus ophthalmology. In this mechanism, CNNs are utilized to extract pertinent features from the images, and LSTM is subsequently employed to discern these extracted features. To validate the effectiveness of the proposed method, a diverse dataset of 2000 experimental fundus images is collected from various sources. These images are categorized into four distinct classes in an equitable manner. Quality assessment techniques are then applied to this dataset. The hybrid deep AMDNet23 model proposed in this study achieves successful detection of AMD ocular disease with an achieved accuracy of 96.50 %. Moreover, the system's prowess is benchmarked against 13 other pre-trained CNN models, effectively illuminating its supremacy in AMD ocular disease diagnosis within the realm of fundus imagery datasets. This exhaustive comparison underscores the method's immense potential and reaffirms its position at the forefront of cutting-edge ocular health diagnostics

Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches

In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE

Dual Band Antenna Design and Prediction of Resonance Frequency Using Machine Learning Approaches

An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed and evaluated in this paper. This article covers how to evaluate the performance of the designed antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit model, and the implementation of machine learning approaches. The MPA’s measured frequency range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio (CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively. The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the measured return loss of the prototype is compared with the simulated return loss observed by using CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS, and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression, ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant frequency of the patch antenna. The performance of the seven ML models is evaluated based on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%, RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted results using machine learning approaches

Dual Band Antenna Design and Prediction of Resonance Frequency Using Machine Learning Approaches

An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed and evaluated in this paper. This article covers how to evaluate the performance of the designed antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit model, and the implementation of machine learning approaches. The MPA’s measured frequency range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio (CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively. The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the measured return loss of the prototype is compared with the simulated return loss observed by using CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS, and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression, ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant frequency of the patch antenna. The performance of the seven ML models is evaluated based on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%, RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted results using machine learning approaches