A possible general mechanism for ultrasound-assisted extraction (UAE) suggested from the results of UAE of chlorogenic acid from Cynara scolymus L. (artichoke) leaves

The use of ultrasound-assisted extraction (UAE) for the extraction of Chlorogenic Acid (CA) from Cynara scolymus L., (artichoke) leaves using 80% methanol at room temperature over 15 minutes gave a significant increase in yield (up to a 50%) compared with maceration at room temperature and close to that obtained by boiling over the same time period. A note of caution is introduced when comparing UAE with Soxhlet extraction because, in the latter case, the liquid entering the Soxhlet extractor is more concentrated in methanol (nearly 100%) that the solvent in the reservoir (80% methanol) due to fractionation during distillation. The mechanism of UAE is discussed in terms of the effects of cavitation on the swelling index, solvent diffusion and the removal of a stagnant layer of solvent surrounding the plant material

Parallel Query Processing on 2D Mesh and Linear Array Architectures

As the size of the web grows, it is necessary to parallelize the process of retrieving information from the web. Incorporating parallelism in search engines is one of the approaches towards achieving this aim. This paper presents an algorithm for query processing on the 2D mesh architecture and two algorithms for linear array architectures. We attempt to exploit the arrangement of processors and the communication pattern in both 2D mesh and linear array architectures to attain high speedup and efficiency for queries-keywords comparisons. A cost model is presented for each algorithm based on both processing and communication cost. Proposed algorithms are evaluated using speedup and efficiency performance metrics. For the same number of processors, 2D Mesh_QP outperforms both linear array algorithms (LA_QPAKP and LA_QPKE). Keywords: 2D Mesh, Linear Arrays, Parallel computing, Query processin

A Survey of Sentiment Analysis and Sarcasm Detection: Challenges, Techniques, and Trends

In recent years, more people have been using the internet and social media to express their opinions on various subjects, such as institutions, services, or specific ideas. This increase highlights the importance of developing automated tools for accurate sentiment analysis. Moreover, addressing sarcasm in text is crucial, as it can significantly impact the efficacy of sentiment analysis models. This paper aims to provide a comprehensive overview of the conducted research on sentiment analysis and sarcasm detection, focusing on the time from 2018 to 2023. It explores the challenges faced and the methods used to address them. It conducts a comparison of these methods. It also aims to identify emerging trends that will likely influence the future of sentiment analysis and sarcasm detection, ensuring their continued effectiveness. This paper enhances the existing knowledge by offering a comprehensive analysis of 40 research works, evaluating performance, addressing multilingual challenges, and highlighting future trends in sarcasm detection and sentiment analysis. It is a valuable resource for researchers and experts interested in the field, facilitating further advancements in sentiment analysis techniques and applications. It categorizes sentiment analysis methods into ML, lexical, and hybrid approaches, highlighting deep learning, especially Recurrent Neural Networks (RNNs), for effective textual classification with labeled or unlabeled data

Automated detection of fake news

During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance

Enhancing loan fraud detection process in the banking sector using data mining techniques

Ongoing loan fraud is a source of concern for financial institutions, as it has a direct financial impact and also scares off customers. This pattern, which can be traced to the development of modern technology, the introduction of novel ideas, and the quickening pace of international connections, makes the detection of fraud an expensive endeavour. This article proposes a novel framework for enhancing the fraud detection of loan banking using data mining algorithms. The framework extracts a number of predictive analysis techniques for identifying loan fraud. Several methods employing a wide range of pipeline architectures have been tried in order to select the optimal champion model. Autotuning has also been used to find the best possible setting for the model’s hyperparameters. The results of the evaluation show that autoencoder with gradient boosting outperformed the other classification algorithms with an accuracy of 98.62%. The proposed framework has the potential to significantly improve the fraud detection process of loan banking, which can ultimately lead to better faster fraud detects rates by combining data mining techniques with dimensionality reduction strategies in the feature space

Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of  Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf  Vectorizer the best to all results than count Vectorizer

Graph Mining for Software Fault Localization: An Edge Ranking based Approach

Fault localization is considered one of the most challenging activities in the software debugging process. It is vital to guarantee software reliability. Hence, there has been a great demand for automated methods that can pinpoint faults for software developers. Various fault localization techniques that are based on graph mining have been proposed in the literature. These techniques rely on detecting discriminative sub-graphs between failing and passing traces. However, these approaches may not be applicable when the fault does not appear in a discriminative pattern. On the other hand, many approaches focus on selecting potentially faulty program components (statements or predicates) and then ranking these components according to their degree of suspiciousness. One of the difficulties encountered by such approaches is to understand the context of fault occurrence. To address these issues, this paper introduces an approach that helps in analyzing the context of execution traces based on control flow graphs. The proposed approach uses the edge-ranking of basic blocks in software programs using Dstar that proved to be more effective than many fault localization techniques. The proposed method helps in detecting some types of faults that could not be previously detected by many other approaches. Using Siemens benchmark, experiments show the effectiveness of the proposed technique compared to some well-known approaches such as Dstar, Tarantula, SOBER, Cause Transition and Liblit05. The percentage of localized faulty versions versus the percentage of code examined is taken as a measure. For instance, when the percentage of examined code is 30%, the proposed technique can localize nearly 81% of the faulty versions, which outperforms the other four techniques

Design, synthesis, and biological profile of novel N-(5-aryl-1,3,4-thiadiazol-2-yl) hydrazinecarboxamides

New series of arylthiadiazole hydrazinecarboxamides (5a-e) have been synthesized by hydrazinolysis of carbamates (4a-e) and spectrally characterized. The new candidates have been screened for their anticonvulsant and immunomodulatory activities. Compound 5e was the most potent anticonvulsant candidate as it showed 100% protection against both maximal electroshock seizure (MES) and subcutaneous pentylenetetrazole (scPTZ) screens without neurotoxicity at 100 mg/kg (0.318 mmol/kg). With respect to immunomodulation, compounds 5a and 5d revealed immunostimulatory activity while compounds 5b, 5c, and 5e had immunosuppressive responses based on ELISA detection of IgM and IgG levels, counting the total mesenteric lymph nodes lymphocytes, and histo-pathological examinations

Graph Mining for Software Fault Localization: An Edge Ranking based Approach

Fault localization is considered one of the most challenging activities in the software debugging process. It is vital to guarantee software reliability. Hence, there has been a great demand for automated methods that can pinpoint faults for software developers. Various fault localization techniques that are based on graph mining have been proposed in the literature. These techniques rely on detecting discriminative sub-graphs between failing and passing traces. However, these approaches may not be applicable when the fault does not appear in a discriminative pattern. On the other hand, many approaches focus on selecting potentially faulty program components (statements or predicates) and then ranking these components according to their degree of suspiciousness. One of the difficulties encountered by such approaches is to understand the context of fault occurrence. To address these issues, this paper introduces an approach that helps in analyzing the context of execution traces based on control flow graphs. The proposed approach uses the edge-ranking of basic blocks in software programs using Dstar that proved to be more effective than many fault localization techniques. The proposed method helps in detecting some types of faults that could not be previously detected by many other approaches. Using Siemens benchmark, experiments show the effectiveness of the proposed technique compared to some well-known approaches such as Dstar, Tarantula, SOBER, Cause Transition and Liblit05. The percentage of localized faulty versions versus the percentage of code examined is taken as a measure. For instance, when the percentage of examined code is 30%, the proposed technique can localize nearly 81% of the faulty versions, which outperforms the other four techniques

Secondary metabolites and pharmacology of Foeniculum vulgare Mill

ABSTRACT From hexane extract of Foeniculum vulgare Mill. Subsp. piperitum the fatty acids, hydrocarbons and sterols were identified. The furocoumarins imperatorin, psoralen, bergapten, xanthotoxin and isopimpinellin were isolated from the methylene chloride extract. The flavonoids isorhamnetin 3-O-α-rhamnoside, quercetin and kaempferol were isolated from the ethyl acetate extract, whereas quercetin 3-Orutinoside, kaempferol 3-O-rutinoside and quercetin 3-O-β-glucoside were isolated from the methanol extract. The crude hexane, methylene chloride, ethyl acetate and methanol extracts of this plant showed antinociceptive and anti-inflammatory activity