Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in OCR systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the KFUPM Handwritten Arabic TexT (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.

Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced.

The Raman (3500−100 cm -1 ) and mid-infrared (4000−400 cm -1 ) spectra in addition to the 1 H NMR chemical shifts (δ, ppm) of ((N 1 Z, N 4 E)-N 1 , N 4 -bis (4 (dimethylamino) benzylidene) butane 1,4-diamine) Schiff base (Molecular formula, C22H30N4) has been recorded. Moreover, we have carried out full geometry optimization followed by frequency calculations using the DFT-B3LYP method employing 6-31+G(d) basis set to include the polarization and diffusion functions. The Raman activities and infra intensities favour a slightly distorted symmetric molecule with an inversion center. Aided by the calculated wavenumbers assembled with Raman and infrared spectral observations, we have provided complete/reliable vibrational assignments for all fundamentals with the exception of those anticipated below 100 and 400 cm -1 , respectively, this is true regardless of whether or not any fundamentals were found either overlapped or coincident. Analyses of normal coordinates provide support for the prevailing spectral interpretations, that were based on the computed atomic displacements in x, y and z Cartesian coordinate (ADCC) from B3LYP/6-31+G(d). All results are reported herein and compared with similar molecules whenever appropriate.

Three Gemini cationic surfactants (GI-surfactants) of different hydrophobic chain lengths based on di-imine compound, abbreviated as GI-6, GI-12 and GI-14, were synthesized and characterized using FT-IR and 1 HNMR. The surface-active parameters calculated in acidic media were discussed. The inhibition performance of GI-surfactants for X65-steel corrosion was assessed by weight loss and electrochemical techniques in 1M HCl and was accompanied by surface analysis and theoretical studies. The resistance of X65-steel was enhanced to nearly ⁓764 ohm.cm 2 after adding 1x10 -3 M of GI-14. This inferred a protective film formation on the X65-steel surface via adsorption phenomena that followed the Langmuir isotherm. The GIsurfactants' inhibition efficiency exceeded 95% at room temperature and 93 % at 328 K owing to the electron-rich centers' presence in their chemical structures. The relation between the prepared GI-surfactants molecular structures and their corrosion inhibition performance was studied theoretically based on DFT and MCs methods. The safeguard effect of GI-surfactants was confirmed by SEM and EDX. The comparison study between the GI-surfactants' performance and the previously reported compounds confirmed their high potential applications as corrosion inhibitors.

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