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This research proposes a three-layer image-spam filtering system. The system filters the image spam by analyzing both the mail header and image. We elaborate the structure of the model and explicate carefully our idea of the design and many technologies related to the model. Experimental results show that this system has satisfactory filtering effect.

Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, their collapse. Vibration-based structural health monitoring techniques have become the most promising alternatives for efficiently detecting and locating damage into civil structures. In this regard, this paper presents a new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Río Papaloapan bridge (RPB) using vibration signals. It is based on the extraction of a set of statistical time features (STFs) from vibration signals, which are analyzed using the autocorrelation function (ACF) to denoise and strengthen the features found in them. Then PCA-based models are computed by using the STFs to enhance the damage location process. Then a new damage index based on MD is proposed to indicate if a damage exists and its location.

Crack identification of buildings using the Internet of things (IoT) is done by continuously monitoring the building structures that provide an early indication of cracks in buildings. The established IoT system constantly gathers structural information using sensors and stores it on a cloud server. This paper presented an innovative machine learning crack identification methodology for detecting cracks using the sensor data. Initially, the collected sensor data is pre-processed by the cloud server using the data fusion process for further processing. Subsequently, effective damage sensitive features such as mode structure (MS) features such as damage signature, streamlined damage signature index, modal assurance criterion (MAC) and coordinate MAC, improved natural frequency (INF) features, and mode structure curvature (MSC) features with curvature damage factor are extracted from the pre-processed data to differentiate cracks easily. After features are extracted, the feature score-based random projection (FSRP) technique is utilized for dimensionality reduction. Finally, hybridization of improved convolutional neural network with modified whale optimization (ICNN-MWO) detects the cracks in the civil structure utilizing the selected features. These effective classification results might alert the user when a high severity or damage is likely to occur. The implementation platform used in this work is PYTHON. The experimental outcomes of the presented technique proved that the presented work is significantly better in terms of various effective performance measures like accuracy (99.93%), mean squared error (3%), precision (99.91%), recall (99.90%), and F-measure (99.9%). The experimental results of the presented methodology provide improved performance than the existing crack identification techniques.

Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.

This paper addresses the challenging task of word spotting in Arabic handwritten documents. We proposed a novel feature that we called Spatial Distribution of Ink at Keypoints (SDIK). The proposed feature captures the characteristics of Arabic handwriting concentrated at endpoints and branch points. SDIK feature quantizes the spatial repartition of ink pixels in the neighborhoods of keypoints. The resulting SDIK features are very fast to match, we take this advantage to match a query word with lines images rather than words images. By this matching mechanism, we overcome the hard task of segmenting an Arabic document into words. The method proposed in this study is tested on historical Arabic document with IBN SINA dataset and on modern handwriting with IFN/ENIT database. The obtained results are great of interest for retrieving query words in an Arabic document.

Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The features namely: Second-Order Difference Plot (SODP) area, Analytic Signal Representation (ASR) area, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth and Fourier–Bessel expansion (FBE)- based mean frequency computed from the Intrinsic Mode Functions (IMFs) are extracted to discriminate normal and CAD subjects. Thereafter, Kruskal–Wallis statistical test is performed on these features. The features having p-value less than 0.05 are considered to be significant. Our results show that three features namely: AM bandwidth, FM bandwidth and FBE-based mean frequency are more suitable than ASR area and SODP area features for discrimination of normal and CAD subjects.

Hospitals and clinics produce a great number of medical images that are stored in large databases. Content-based medical image retrieval systems (CBMIRs) is one of the solutions to access rapidly and efficiently to these databases using their visual content. In this paper, we propose a new algorithm for medical image indexing. It consists on a combination of bio-orthogonal CDF wavelet transform (WT) based on lifting scheme and principal component analysis (PCA). We use this WT to decompose images, then we apply the PCA method to reduce the number of features and select the pertinent components which represent image signature. Finally, Euclidean distance is used to retrieve most similar image from databases when query image is submitted. We have tested our algorithm on the retinal image database. The results obtained by our algorithm are compared with several published methods cited in the literature and shows an efficiency of 80%, which is significantly higher and much faster than recent methods in CBMIRs domain.

Wire arc additive manufacturing (WAAM) is a rapidly growing technology that offers several advantages over traditional manufacturing methods, such as high deposition rates and the ability to build large components in a cost-effective manner. However, WAAM is also prone to the generation of defects, so the timely identification of anomalies is important to reduce the waste and get components of high quality. To develop anomaly detection application, the feature extraction process represents a key ingredient which allows machine learning systems to analyze big data. Waveform GMAW welding processes are typically used in WAAM to reduce the heat input supplied to the material and avoid defects such as excessive bending of parts and residual stress. These processes are based on the controlled dip transfer principle, so the waveforms should repeat themselves during deposition. This suggests that the frequency content of the voltage and current welding signals acquired during the process can provide important information about the process state. In this research, an experimental campaign was conducted to collect data for pulsed welding and surface tension transfer (STT) processes during the deposition of mild steel ER70S6, stainless steel 316L, Aluminum 4043, and Inconel 718 alloys. Welding voltage and current signals were acquired during the building processes, and a frequency domain analysis was conducted using the Fast Fourier transform (FFT) and discrete wavelet transform (DWT) with the aim to extract features from signals aiming to better separate the feature space, which means improve anomaly detection performance in detecting defects like arc instability, porosity, geometrical defect due to arc blow and humping. Furthermore, a methodology based on time-frequency analysis enhanced by Gabor filter for texture anomaly detection of scalograms obtained by Morlet Continuous Wavelet Transform is proposed, which showed an improvement of performance in separation between normal and anomalous deposition of several materials under different welding technologies.

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the first step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To differentiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To confirm the system’s performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and specificity of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

The research work has focused on detection and prediction of melanoma which is done by subjecting to features extraction, where the features of an image consisting of melanoma regions are detected by analysis and this analysis is done by considering the features like color and texture-based features learning strategy. These features are extracted by combining color and texture-based features extraction with deep convolutional features representation learning strategy. The colors of images are extracted by representing the colors of different channels into red, green and blue channel information. The combination of texture features extraction with color-based features extraction in addition to Alex net features extraction learning has made the system more robust and efficient toward the segmentation and classification of images. Further, the erected method involves convoluting the features of extracted information with color and texture-based method which has led our system to full convolution neural networks with images features extraction. The melanoma is detected and segmented with watershed segmentation, these segmented features are subjected to the proposed features extraction method, where the features are extracted by combining the methods of texture with color-based information. These colors are made available to the proposed method by analyzing the regions of melanoma images. The erected method does the task of features extraction by Weber law descriptors in combination with red, green, blue channels information extracted from features representation learning. The proposed method has yielded an accuracy of 94.12% of segmentation accuracy and a classification accuracy of 94.32% with respect to various other classification techniques.