Discovering Regression Rules with Ant Colony Optimization

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible

Attention focussing and anomaly detection in real-time systems monitoring

In real-time monitoring situations, more information is not necessarily better. When faced with complex emergency situations, operators can experience information overload and a compromising of their ability to react quickly and correctly. We describe an approach to focusing operator attention in real-time systems monitoring based on a set of empirical and model-based measures for determining the relative importance of sensor data

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data

Improvement in Renal Function and Reduction in Serum Uric Acid with Intensive Statin Therapy in Older Patients: A Post Hoc Analysis of the SAGE Trial.

BackgroundImprovement in renal function and decreases in serum uric acid (SUA) have been reported following prolonged high-intensity statin (HMG-CoA reductase inhibitor) therapy. This post hoc analysis of the SAGE trial examined the effect of intensive versus less intensive statin therapy on renal function, safety, and laboratory parameters, including SUA, in elderly coronary artery disease (CAD) patients (65-85 years) with or without chronic kidney disease (CKD).MethodsPatients were randomized to atorvastatin 80 mg/day or pravastatin 40 mg/day and treated for 12 months. Patients were stratified using Modification of Diet in Renal Disease (MDRD) estimated glomerular filtration rates (eGFRs) in CKD (eGFR <60 mL/min/1.73 m(2)) and non-CKD populations.ResultsOf the 893 patients randomized, 858 had complete renal data and 418 of 858 (49%) had CKD (99% Stage 3). Over 12 months, eGFR increased with atorvastatin and remained stable with pravastatin (+2.38 vs. +0.18 mL/min/1.73 m(2), respectively; p < 0.0001). MDRD eGFR improved significantly in both CKD treatment arms; however, the increased eGFR in patients without CKD was significantly greater with atorvastatin (+2.08 mL/min/1.73 m(2)) than with pravastatin (-1.04 mL/min/1.73 m(2)). Modest reductions in SUA were observed in both treatment arms, but a greater fall occurred with atorvastatin than with pravastatin (-0.52 vs. -0.09 mg/dL, p < 0.0001). Change in SUA correlated negatively with changes in eGFR and positively with changes in low-density lipoprotein cholesterol. Reports of myalgia were rare (3.6% CKD; 5.7% non-CKD), and there were no episodes of rhabdomyolysis. Elevated serum alanine and aspartate transaminase to >3 times the upper limit of normal occurred in 4.4% of atorvastatin- and 0.2% of pravastatin-treated patients.ConclusionIntensive management of dyslipidemia in older patients with stable coronary heart disease may have beneficial effects on renal function and SUA

Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery

Many data mining algorithms do not make use of existing domain knowledge when constructing their models. This can lead to model rejection as users may not trust models that behave contrary to their expectations. Semantic constraints provide a way to encapsulate this knowledge which can then be used to guide the construction of models. One of the most studied semantic constraints in the literature is monotonicity, however current monotonically-aware algorithms have focused on ordinal classification problems. This paper proposes an extension to an ACO-based regression algorithm in order to extract a list of monotonic regression rules. We compared the proposed algorithm against a greedy regression rule induction algorithm that preserves monotonic constraints and the well-known M5’ Rules. Our experiments using eight publicly available data sets show that the proposed algorithm successfully creates monotonic rules while maintaining predictive accuracy

A new generation of intelligent trainable tools for analyzing large scientific image databases

The focus of this paper is on the detection of natural, as opposed to human-made, objects. The distinction is important because, in the context of image analysis, natural objects tend to possess much greater variability in appearance than human-made objects. Hence, we shall focus primarily on the use of algorithms that 'learn by example' as the basis for image exploration. The 'learn by example' approach is potentially more generally applicable compared to model-based vision methods since domain scientists find it relatively easier to provide examples of what they are searching for versus describing a model

Forensic Analysis of WhatsApp SQLite Databases on the Unrooted Android Phones

WhatsApp is the most popular instant messaging mobile application all over the world. Originally designed for simple and fast communication, however, its privacy features, such as end-to-end encryption, eased private and unobserved communication for criminals aiming to commit illegal acts. In this paper, a forensic analysis of the artefacts left by the encrypted WhatsApp SQLite databases on unrooted Android devices is presented. In order to provide a complete interpretation of the artefacts, a set of controlled experiments to generate these artefacts were performed. Once generated, their storage location and database structure on the device were identified. Since the data is stored in an encrypted SQLite database, its decryption is first discussed. Then, the methods of analyzing the artefacts are revealed, aiming to understand how they can be correlated to cover all the possible evidence. In the results obtained, it is shown how to reconstruct the list of contacts, the history of exchanged textual and non-textual messages, as well as the details of their contents. Furthermore, this paper shows how to determine the properties of both the broadcast and the group communications in which the user has been involved, as well as how to reconstruct the logs of the voice and video calls. Doi: 10.28991/HIJ-2022-03-02-06 Full Text: PD

An Assessment of Students’ Preferences Using Social Media Platforms on Their Selection of Private Universities in Lebanon

This paper assesses the behavior of prospective students of private universities in Lebanon on social media platforms; classifies them according to the extent and the way they use these platforms, and investigates students’ preferences using social media to choose a private University. A quantitative approach was adopted using a structured questionnaire administered to a convenient sample of 527 students from private universities in Lebanon who responded willingly. Collected data were analyzed using the SPSS program employing descriptive statistics. The university sample consisted of six Lebanese Higher Education Institutions that belong to different socio-economic categories, diverse cultures, many geographic locations, and varying chronological seniority. Results showed the influence of university pages on these platforms on students’ decisions and their adoption as a primary source of information before making their choices. Universities were unsuccessful in providing an effective communication channel with students or enabling two-way communication and motivating participation in comments, content creation, sharing experiences related to the universities, or sharing posts from their pages. Authors recommend universities review their motivational schemes for students to interact on university platforms regarding access to information and communication. For marketing, it is necessary to hire qualified staff, and provide a consistent and updated policy

SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys

Study the Efficiency of Two Concentrations from Algae Cladophora glomerata Extract on the Giardia lamblia parasite

خلال الدراسة الحالية تم عزل طفيلي Giardia lamblia من عينات البراز لمرضى مصابين باسهال Giardiasis  حيث تم عزل الطفيلي و تنميته باستخدام وسط HSP . استعان الباحثون بفئران تجريبيه بواقع اربع مجاميع من الفئران وذلك لتقييم فعالية تركيزين (128,256)ملغم /مل من مستخلص كلوروفورم للطحالب الخضراء Cladophora glomerata ضد الطفيلي المعزول ومقارنة بالعلاج التجاري للطفيلي) (Flagyl وذلك بقياس بعض المؤشرات مثل انزيمات الكبد GPTand GOT)) , مستوى تراكيز الصوديوم والبوتاسيوم والحديد بالدم اضافة الى تعداد اكياس الطفيلي لبراز كل مجموعة من مجاميع الفئران المستخدمة خلال التجربة , اظهرت النتائج انحدار في مستويات انزيمات الكبد بعد معالجة الفئران المصابة بالطفيلي بمستخلص الطحالب. بينما اشرت قياسات مستوى الصوديوم والبوتاسيوم و الحديد زيادة بعد العلاج بمستخلص الطحلب .وبالنسبة لتعداد اكياس الطفيلي فقد قل تعدادها في براز الفئران المصابة بعد تجريعها فمويا بمستخلص الطحلب مقارنة بالعلاج التجاري .واخيرا تم الكشف عن المركبات الفعالة في مستخلص الطحالب المدروسة باجراء فحص  GC-Mass حيث اظهرت نتائج الفحص وجود العديد من المركبات ذات فعالية بايولوجية متنوعة . تعتبر هذه الدراسة الاولى على مستوى العالم لبيان امكانية استخدام المركبات الفعالة بايولوجيا الموجوده في طحلب Cladophora glomerata كعلاج مناسب وبديل عن العلاج المصنع للقضاء على اصابات الطفيلي Giardia.Giardia lamblia parasite was isolated from the diarrhea samples of patients with Giardiasis dysentery and was developed in HSP media, four mice groups have been used to find in vivo efficacy of two concentrations (128,256) mg/ml of chlorophorm extracts from Cladophora glomerata algae against Giardia lamblia parasite  as compared with (Flagyl) by measuring several biochemical markers as ( GPT and GOT) enzymes ,sodium ,potassium and iron concentration as well as counting the number of parasitic cysts in each mice groups. The results demonstrate that levels of GPTA GOT enzymes have been decreased in mice treated with algal extract. As for the concentration of the Sodium, Potassium and Iron increased in mice treated with algal extract. The number of the Giardia cyst is also reduced in orally inoculated mice with both concentrations of algal extract as compared with positive control and the Flagyl treated group. In terms of bioactive compounds, GC-Mass results indicate the presence of many phytochemicals with different biologically active properties This study represents the first attempt to use Cladophora glomerata derived from phytochemicals to treat giardiasis in vivo