ONE3A: one-against-all authentication model for smartphone using GAN network and optimization techniques.

This study focuses on addressing computational limits in smartphones by proposing an efficient authentication model that enables implicit authentication without requiring additional hardware and incurring less computational cost. The research explores various wrapper feature selection strategies and classifiers to enhance authentication accuracy while considering smartphone limitations such as hardware constraints, battery life, and memory size. However, the available dataset is small; thus, it cannot support a general conclusion. In this article, a novel implicit authentication model for smartphone users is proposed to address the one-against-all classification problem in smartphone authentication. This model depends on the integration of the conditional tabular generative adversarial network (CTGAN) to generate synthetic data to address the imbalanced dataset and a new proposed feature selection technique based on the Whale Optimization Algorithm (WOA). The model was evaluated using a public dataset (RHU touch mobile keystroke dataset), and the results showed that the WOA with the random forest (RF) classifier achieved the best reduction rate compared to the Harris Hawks Optimization (HHO) algorithm. Additionally, its classification accuracy was found to be the best in mobile user authentication from their touch behavior data. WOA-RF achieved an average accuracy of 99.62 ± 0.40% with a reduction rate averaging 87.85% across ten users, demonstrating its effectiveness in smartphone authentication.

The effect of epidural steroid injection during surgery on surgical site infections following lumbar fusion.

Background: Intraoperative epidural steroid injections (ESIs) have been suggested to limit pain following lumbar fusions. However, the frequency of resultant surgical site infections has not been fully investigated. Methods: We retrospectively followed two groups of patients; 23 patients were the control group, while the other 23 patients received, in addition to the spinal fusions, intraoperative ESI. Results: Patients in the latter ESI/fusion treatment group had significantly increased rates of superficial and deep infections (i.e., superficial infections 17.4% and 4.3% deep infections) versus control patients (i.e., 8.6% superficial and 0% deep) undergoing fusions alone. Conclusion: We observed an increased risk of postoperative surgical site infections among patients who underwent intraoperative ESI in addition to their lumbar fusions.

Should Redo Surgery be Offered to Patients with Relapsed Glioblastoma? - Outcome Analyses of a Single Institution Comparative Cohort Study.

BACKGROUND: Glioblastoma multiforme (GBM) is the predominant malignant brain tumor originating intracranially. The established first-line treatment postsurgery is concurrent chemoradiation as a definitive measure. However, recurrent GBM's pose a challenge for clinicians who rely on institutional experience to determine the most suitable course of action. Second-line chemotherapy may be administered with or without surgery depending on the institution's practice. This study aims to present our tertiary center institution's experience with recurrent GBM patients who underwent redo surgery. METHODS: In this retrospective study we analyzed the surgical and oncological data of patients with recurrent GBM who underwent redo surgery at the Royal Stoke University Hospitals between 2006 and 2015. The group 1 (G1) comprised the reviewed patients, while a control group (G2) was randomly selected, matching the reviewed group by age, primary treatment, and progression-free survival (PFS). The study collected data on various parameters, including overall survival, PFS, extent of surgical resection, and postoperative complications. RESULTS: This retrospective study included 30 patients in G1 and 32 patients in G2, matched based on age, primary treatment, and PFS. The study found that the overall survival for the G1 group from the time of first diagnosis was 109 weeks (45-180) compared to 57 weeks (28-127) in the G2 group. The incidence of postoperative complications after the second surgery was 57%, which included hemorrhage, infarction, worsening neurology due to edema, cerebrospinal fluid leak, and wound infection. Furthermore, 50% of the patients in the G1 group who underwent redo surgery received second-line chemotherapy. CONCLUSIONS: Our study found that redo surgery for recurrent GBM is a viable treatment option for a select group of patients with good performance status, longer PFS from primary treatment, and compressive symptoms. However, the use of redo surgery varies depending on the institution. A well-designed randomized controlled trial in this population would help establish the standard of surgical care.

Efficient thermal face recognition method using optimized curvelet features for biometric authentication.

Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning.

Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review.

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Duroplasty in iatrogenic dorsal spinal cord herniation: illustrative case.

BACKGROUND: The case report detailed an unusual presentation of an iatrogenic dorsal cord herniation at the level of the thoracic cord after insertion of an epidural catheter 8 months before presentation to the neurosurgical clinic. OBSERVATIONS: Only 13 cases of iatrogenic dorsal cord herniation, most of which occurred after spinal surgery, have been described in the literature. This was the first case of a spinal cord hernia described after the insertion of an epidural catheter. In this case study, the authors described a 38-year-old man who presented with progressive lower limb weakness, sensory deficits, perianal numbness, and urinary/fecal incontinence. He was diagnosed with a spinal cord hernia that reherniated after an initial sandwich duroplasty repair. Definitive repair was made after his re-presentation using an expansile duroplasty. LESSONS: In patients with previous spinal instrumentation who present with neurological symptoms, spinal cord herniation should be considered a likely differential despite its rarity. In this case, a simple duroplasty was insufficient to provide full resolution of symptoms and was associated with recurrence. Perhaps a combination of graft and expansile duroplasty may be used for repair, especially when associated with a tethered cord and in the presence of significant adhesions.

New fractional-order shifted Gegenbauer moments for image analysis and recognition.

Orthogonal moments are used to represent digital images with minimum redundancy. Orthogonal moments with fractional-orders show better capabilities in digital image analysis than integer-order moments. In this work, the authors present new fractional-order shifted Gegenbauer polynomials. These new polynomials are used to define a novel set of orthogonal fractional-order shifted Gegenbauer moments (FrSGMs). The proposed method is applied in gray-scale image analysis and recognition. The invariances to rotation, scaling and translation (RST), are achieved using invariant fractional-order geometric moments. Experiments are conducted to evaluate the proposed FrSGMs and compare with the classical orthogonal integer-order Gegenbauer moments (GMs) and the existing orthogonal fractional-order moments. The new FrSGMs outperformed GMs and the existing orthogonal fractional-order moments in terms of image recognition and reconstruction, RST invariance, and robustness to noise.

Unleash multifunctional role of long noncoding RNAs biomarker panel in breast cancer: a predictor classification model.

Aim: We aimed to explore the circulating expression profile of nine lncRNAs (MALAT1, HOTAIR, PVT1, H19, ROR, GAS5, ANRIL, BANCR, MIAT) in breast cancer (BC) patients relative to normal and risky individuals. Methods: Serum relative expressions of the specified long non-coding RNAs were quantified in 155 consecutive women, using quantitative reverse-transcription PCR. Random Forest (RF) and decision tree were also applied. Results: Significant MALAT1 upregulation and GAS5 downregulation could discriminate risky women from healthy controls. Overexpression of the other genes showed good diagnostic performances. Lower GAS5 levels were associated with metastasis and recurrence. RF model revealed a better performance when combining gene expression patterns with risk factors. Conclusion: The studied panel could be utilized as diagnostic/prognostic biomarkers in BC, providing promising epigenetic-based therapeutic targets.

Late-Onset Post-Traumatic Spinal Cord Herniation as a Rare and Overlooked Cause of Late Neurologic Deterioration After Penetrating Injury to the Thoracic Spine: A Case Report and Review of the Literature.

BACKGROUND AND CASE DESCRIPTION: We report a case of an 18-year-old patient who presented with late progressive deterioration of neurologic condition 8 weeks after a penetrating injury to the back. Investigations revealed a dorsally located post-traumatic spinal cord herniation. Urgent exploration, decompression, and repair were performed. We reviewed the literature and found only 19 similar cases previously reported. Pathophysiology and presentations were variable and even poorly understood. CONCLUSIONS: Late-onset post-traumatic spinal cord herniation is a potentially curable cause of neurologic deterioration after spinal trauma and should be considered in all cases with late neurologic deterioration after spinal trauma.

A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation.

This paper presents a method for breast cancer diagnosis in digital mammogram images. Multi-resolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.

The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process.

The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicillin and cloxacillin in aqueous solution in terms of COD removal. The configuration of the backpropagation neural network giving the smallest mean square error (MSE) was three-layer ANN with tangent sigmoid transfer function (tansig) at hidden layer with 14 neurons, linear transfer function (purelin) at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). ANN predicted results are very close to the experimental results with correlation coefficient (R(2)) of 0.997 and MSE 0.000376. The sensitivity analysis showed that all studied variables (reaction time, H(2)O(2)/COD molar ratio, H(2)O(2)/Fe(2+) molar ratio, pH and antibiotics concentration) have strong effect on antibiotic degradation in terms of COD removal. In addition, H(2)O(2)/Fe(2+) molar ratio is the most influential parameter with relative importance of 25.8%. The results showed that neural network modeling could effectively predict and simulate the behavior of the Fenton process.

A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram.

This paper presents a comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands. A set of the biggest coefficients from each decomposition level is extracted. Then a supervised classifier system based on Euclidian distance is constructed. The performance of the classifier is evaluated using a 2 x 5-fold cross validation followed by a statistical analysis. The experimental results suggest that curvelet transform outperforms wavelet transform and the difference is statistically significant.

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform.

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.