SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions

Body mass index and variability in meal duration and association with rate of eating

BackgroundA fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day.MethodsUsing an observational cross-sectional study design, non-smoking participants aged 18–45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating.ResultsParticipants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96).ConclusionShorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors

Deep Learning-Based Comparative Study to Detect Polyp Removal in Endoscopic Images

Polyps are one of the most common gastrointestinal diseases. It has the potential to cause fatal colon and rectal cancers. As a result, it must be removed during the primitive stage. In this paper, we developed an algorithm that uses endoscopy images to detect polyp removal status. We investigated convolutional neural networks such as DenseNet, ResNet, VGG, MobileNet, and others to extract features from images and then use those features to classify whether a polyp is completely removed or not. 1000 dyed resection margins and 1000 dyed and lifted polyps' images from a publicly available dataset were used to test and train the proposed models. On the testing dataset, we obtained 85% sensitivity, 88% precision, and 85% fl-scores by using MobileNet architecture. This computer-aided polyp removal method assists physicians in diagnosing polyp status in a reliable, quick, and cost-effective manner

Antioxidant, Anti-Nephrolithe Activities and in Vitro Digestibility Studies of Three Different Cyanobacterial Pigment Extracts

Phycobiliprotein-containing water and carotenoid-containing methanolic extracts of three different cyanobacteria, Pseudanabaena sp., Spirulina sp. and Lyngbya sp., were studied for their DPPH scavenging, iso-bolographic studies, and anti-nephrolithe activities. The best EC50 values for DPPH scavenging were in Lyngbya water (LW, 18.78 ± 1.57 mg·mg−1 DPPH) and Lyngbya methanol (LM, 59.56 ± 37.38 mg·mg−1 DPPH) extracts. Iso-bolographic analysis revealed most of the combinations of extracts were antagonistic to each other, although LM—Spirulina methanol (SM) 1:1 had the highest synergistic rate of 86.65%. In vitro digestion studies showed that DPPH scavenging activity was considerably decreased in all extracts except for Pseudanabaena methanol (PM) and LM after the simulated digestion. All of the extracts were effective in reducing the calcium oxalate crystal size by nearly 60%–65% compared to negative control, while PM and Spirulina water (SW) extracts could inhibit both nucleation and aggregation of calcium oxalate by nearly 60%–80%

Construction of pSM201v: A broad host range replicative vector based on shortening of RSF1010

Despite possessing attractive features such as autotrophic growth on minimal media, industrial applications of cyanobacteria are hindered by a lack of genetic manipulative tools. There are two important features that are important for an effective manipulation: a vector which can carry the gene, and an induction system activated through external stimuli, giving us control over the expression. In this study, we describe the construction of an improved RSF1010-based vector as well as a temperature-inducible RNA thermometer. RSF1010 is a well-studied incompatibility group Q (IncQ) vector, capable of replication in most Gram negative, and some Gram positive bacteria. Our designed vector, named pSM201v, can be used as an expression vector in some Gram positive and a wide range of Gram negative bacteria including cyanobacteria. An induction system activated via physical external stimuli such as temperature, allows precise control of overexpression. pSM201v addresses several drawbacks of the RSF1010 plasmid; it has a reduced backbone size of 5189 bp compared to 8684 bp of the original plasmid, which provides more space for cloning and transfer of cargo DNA into the host organism. The mobilization function, required for plasmid transfer into several cyanobacterial strains, is reduced to a 99 bp region, as a result that mobilization of this plasmid is no longer linked to the plasmid replication. The RNA thermometer, named DTT1, is based on a RNA hairpin strategy that prevents expression of downstream genes at temperatures below 30 °C. Such RNA elements are expected to find applications in biotechnology to economically control gene expression in a scalable manner

Cyanobacterial pigments as natural anti-hyperglycemic agents: An in vitro study

Traditional medicines for controlling postprandial hyperglycemia includes herbs and plant extracts as well as synthetic drugs like acarbose. Synthetic drug molecules frequently have side effects such as flatulence and diarrhea. Cyanobacterial pigments have excellent anti-oxidant and free radical scavenging properties. Thus, α-amylase and α-glucosidase inhibiting activities of purified pigments and crude extracts from three cyanobacterial species, Lyngbya, Microcoleus and Synechocystis sp., were investigated. Lyngbya extract had the highest total anti-oxidant activity (TAC) before digestion (48.26 ± 0.04 µg AAE ml-1) while purified lycopene had the highest TAC after digestion (154.16 ± 0.96 µg AAE ml-1). The Microcoleus extract had the highest ABTS scavenging activity before digestion (98.23 ± 0.25 %) while purified C-phycocyanin (C-PC) had the highest ABTS scavenging after digestion (99.69 ±0.04 %). None of the digested or undigested extracts performed better than acarbose in inhibiting α-amylase but the digested Microcoleus extract was able to inhibit its activity by ~35 %. The purified pigments gave inhibitory activities ranging from ~ 8 – 16 %. The Lyngbya extract had the highest inhibitory activity against α-glucosidase both before and after digestion (62.22 ± 0.02 and 97.82 ± 0.03 % respectively). Purified C-phycoerythrin (C-PE), C-PC, lycopene and myxoxanthophyll could inhibit α-glucosidase in a range of ~83 – 96 %. Considering the potent inhibitory activities of purified pigments against both α-amylase and α-glucosidase, cyanobacterial pigments could be used as food additives for their dual advantage of anti-oxidant and anti-hyperglycemic activities

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research