Toward a self-learned Smart Contracts

In recent years, Blockchain technology has been highly valued and disruptive. Several researches have presented a merge between blockchain and current application i.e. medical, supply chain, and e-commerce. Although Blockchain architecture does not have a standard yet, IBM, MS, AWS offer BaaS (Blockchain as a Service). In addition to the current public chains i.e. Ethereum, NEO, and Cardeno; there are some differences between several public ledgers in terms of development and architecture. This paper introduces the main factors that affect integration of Artificial Intelligence with Blockchain. As well as, how it could be integrated for forecasting and automating; building self-regulated chain.Comment:

Investigation the Effects of Green-Synthesized Copper Nanoparticles on the Performance of Activated Carbon-Chitosan-Alginate for the Removal of Cr(VI) from Aqueous Solution

In the present investigation, green nano-zerovalent copper (GnZVCu), activated carbon (AC), chitosan (CS) and alginate (ALG) nanocomposites were produced and used for the elimination of chromium (VI) from a polluted solution. The nanocomposites GnZVCu/AC-CS-alginate and AC-CS-alginate were prepared. Analysis and characterization were performed by the following techniques: X-ray diffraction, energy dispersive X-ray spectroscopy, scanning electron microscopy, transmission electron microscopy and Fourier transform infrared spectroscopy. The SEM analysis revealed that the nanocomposites are extremely mesoporous, which leads to the greatest adsorption of Cr+6 (i.e., 97.5% and 95%) for GnZVCu/AC-CS-alginate and AC-CS-alginate, respectively. The adsorption efficiency was enhanced by coupling GnZVCu with AC-CS-alginate with a contact time of 40 min. The maximum elimination of Cr+6 with the two nanocomposites was achieved at pH 2. The isotherm model, Freundlich adsorption isotherm and kinetics model and P.S.O.R kinetic models were discovered to be better suited to describe the exclusion of Cr+6 by the nanocomposites. The results suggested that the synthesized nanocomposites are promising for the segregation of Cr+6 from polluted solutions, specially the GnZVCu/AC-CS-alginate nanocomposite

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

Gastrointestinal (GI) cancer comprises esophageal, gastric, colon and rectal tumors. The diagnosis of GI cancer often relies on medical imaging modalities namely magnetic resonance imaging (MRI), histopathological slides, endoscopy, and computed tomography (CT) scans. This provides particular details about the size, location, and characteristics of tumors. The high death rate for GI cancer patients shows that it is possible to increase analysis for a more personalized therapy strategy which leads to improved prognosis and few side effects although many extrapolative and predictive biomarkers exist. Gastrointestinal cancer classification is a challenging but vital area of research and application within medical imaging and machine learning. Artificial intelligence (AI) based diagnostic support system, especially convolution neural network (CNN) based image examination tool, has enormous potential in medical computer vision. The study presents a GI Cancer Detection and Classification utilizing the African Vulture Optimization Algorithm with Transfer Learning (GICDC-AVOADL) methodology. The major aim of the GICDC-AVOADL model is to examine GI images for the identification of cancer. To achieve this, the GICDC-AVOADL method makes use of an improved EfficientNet-B5 method to learn features from input images. Furthermore, AVOA is exploited for optimum hyperparameter selection of the improved EfficientNet-B5 method. The GICDC-AVOADL technique applies dilated convolutional autoencoder (DCAE) For GI cancer detection and classification. A complete set of simulations was conducted to examine the enhanced GI cancer detection performance of the GICDC-AVOADL technique. The extensive results inferred superior performance of the GICDC-AVOADL algorithm over current models

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques

Computational and experimental investigation of antibacterial and antifungal properties of Nicotiana tabacum extracts

The identification of novel anti-infective agents of synthetic and natural origin represents one of the main aims of contemporary drug discovery. In the current work, four different varieties of Nicotiana tabacum, namely, K399, SPG28, Swat No. 1, and Swat No. 2, were studied to assess the antibacterial and antifungal properties of their extracts. The extracts contain anthraquinones, alkaloids, saponins, terpenoids, tannins, resins, steroids, proteins, and carbohydrates, and the antibacterial and antifungal activities were evaluated toward four bacterial and four fungal strains. N. tabacum K399 showed the highest zone of inhibition against E. coli. Similarly, K399 showed the highest antifungal potential, as the highest zone of inhibition for the set was detected against C. albicans. Then, the underlying molecular mechanism was further investigated, and the extracts were tested for their inhibitory potential against urease, an enzyme which is conserved in bacteria and fungi. Additionally, computational tools were enrolled to assess the role of rutin and chlorogenic acid, which are among the main constituents of N. tabacum leaves, in interacting with urease through molecular docking. Combined together, the computational and experimental results support the antibacterial and antifungal potential of N. tabacum extracts, particularly, that obtained from K399 variety

Spotted hyena optimizer with deep learning driven cybersecurity for social networks

Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech. Online provocation, abuses, and attacks are widely termed cyberbullying (CB). The massive quantity of user generated content makes it difficult to recognize CB. Current advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) tools enable to detect and classify CB in social networks. In this view, this study introduces a spotted hyena optimizer with deep learning driven cybersecurity (SHODLCS) model for OSN. The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN. For achieving this, the SHODLCS model involves data pre-processing and TF-IDF based feature extraction. In addition, the cascaded recurrent neural network (CRNN) model is applied for the identification and classification of CB. Finally, the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance. The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches

HEMORRHOIDECTOMY VERSES RUBBER BAND LIGATION

According to some estimates, haemorrhoids afflict up to one-quarter of all individuals. There are numerous methods available to manage them, ranging from topical and medicinal medicines to outpatient treatments and surgical techniques to repair or excise. Given the disease's polysymptomaticism, determining which therapy choice is optimal is tough. Hemorrhoid disease treatment is one of the most difficult fields in general surgery, with various approaches utilised to cure this illness. In this research, we contrasted Hemorrhoidectomy verses Rubber Band Ligation results of treatment methods for hemorrhoids. Review the effectiveness and safety of the two most often used conventional treatments for haemorrhoids, rubber band ligation and excisional haemorrhoidectomy, and compare between the clinical results for both procedures. The PubMed database and EBSCO Information Services were utilized to choose the articles. In this review, all pertinent articles related to both our topic and other articles were used. Other articles that have nothing to do with this subject were not included. The group members looked through a certain format in which the data had been extracted. Internal hemorrhoid is a common pathological anorectal appearance, although it is a difficult condition to treat. Internal haemorrhoids symptoms and indicators should be thoroughly explored, as should clinical grading. Individual thinking and clinical considerations should influence the various possibilities for managing internal haemorrhoids and specific therapeutic approaches. At first, lifestyle changes should be made, such as consuming a high-fiber diet, developing sane bathroom routines, and administering phlebotropic drugs. When alternative treatments don't work, surgical methods and outpatient procedures should be used. Therapy management such as Hemorrhoidectomy or Rubber Band Ligation is critical to preventing future consequences from internal haemorrhoids

Validity and reliability of the Arabic sedentary behavior questionnaire among university students aged between 18–30 years old

Abstract Purpose The study aimed to test the validity and reliability of the Arabic version of the sedentary behavior questionnaire (SBQ). Methods A total of 624 university students (273 males; 351 females, mean age = 20.8 years) were recruited from Taibah University, Madinah, Saudi Arabia. For criterion and constructive validity (n = 352), the Arabic SBQ was compared with total sitting time from the International Physical Activity Questionnaire-short form (IPAQ-SF) and the International Physical Activity Questionnaire-long form (IPAQ-LF). For concurrent validity, the English and Arabic SBQ versions were given concurrently to bilingual university students (n = 122) once. For test–retest reliability, the Arabic SBQ was given twice to participants (n = 150) at a one-week interval. Results Sitting time of IPAQ-SF (7th question: sitting time on weekdays) and IPAQ-LF (21st question: sitting time on weekdays and 22nd question: sitting time on weekends) correlated significantly with total sitting time/week of the Arabic SBQ (r = 0.29, p = 0.003; r = 0.14, p = 0.02, respectively). Motorized transportation measured with the IPAQ-LF correlated significantly with time spent driving in a car, bus, or train from the Arabic SBQ on weekdays and weekends (r = 0.53, p < 0.001; r = 0.44 p < 0.001, respectively). The total sitting time of the Arabic SBQ was inversely correlated with BMI (r = -0.18, p = 0.001). The correlations between the Arabic and the English SBQ versions ranged from 0.25–0.96; p < 0.001 on weekdays and 0.50–0.90; p < 0.001 on weekends. Moderate to good reliability was also found between test and retest for all SBQ items and total score during weekdays (0.72 to 0.8), and weekends (0.64 to 0.87), with exception of the 7th item "play musical instrument", ICC = 0.46). Mean difference of test–retest of the Arabic SBQ was not significantly different from zero for the total sitting time of the Arabic SBQ (t = -0.715, P = 0.476). Conclusion The Arabic SBQ had satisfactory levels of reliability, with total sitting time of the Arabic SBQ correlating significantly with sitting times derived from IPAQ-SF, IPAQ-LF, and the English SBQ versions. Hence, the Arabic SBQ can be used as a tool to measure sedentary behavior among adult Arabs aged between 18 to 30 years old in future epidemiologic and clinical practice