Different clustering techniques : means for improved knowledge discovery

Application of different clustering techniques can result in different basic data set partitions emphasizing diversified aspects of resulting clusters. Since analysts have a great responsibility for the successful interpretation of the results obtained through some of the available tools, and for giving meaning to what forms a qualitative set of clusters, additional information attained from different tools is of a great use to them. In this article we presented the clustering results of small and medium sized enterprises’ (SMEs) data, obtained in DataEngine, iData Analyzer and Weka tools for intelligent analysis

EWMA Based Threshold Algorithm for Intrusion Detection

Intrusion detection is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security. Many intrusions manifest in dramatic changes in the intensity of events occuring in computer networks. Because of the ability of exponentially weighted moving average control charts to monitor the rate of occurrences of events based on their intensity, this technique is appropriate for implementation in threshold based algorithms

EWMA Algorithm in Network Practice

Intrusion detection is used to monitor and capture intrusions into computer and network systems which attempt to compromise their security. Many intrusions manifest in changes in the intensity of events occuring in computer networks. Because of the ability of exponentially weighted moving average (EWMA) control charts to monitor the rate of occurrences of events based on their intensity, this technique is appropriate for implementation in control limits based algorithms. The paper also gives a review of a possible optimization method. The validation check of results will be performed on authentic network samples

First evidence of the presence of Multixenobiotic Resistance Mechanism activity in freshwater invasive species, signal crayfish Pacifastacus leniusculus (Dana, 1852)

Background and Purpose: The signal crayfish Pacifastacus leniusculus (Dana, 1852) is one of the most successful invasive species of crayfish in European freshwaters, an extremely diverse though endangered group of ecosystems. The main goal of this study was to functionally characterize multixenobiotic resistance (MXR) mechanism defense activity in P. leniusculus tissues for the first time. MXR mechanism protects the cell from a wide variety of toxic compounds, and it is mediated by the transport activity of ATP-binding cassette (ABC) proteins. Materials and Methods: MXR transporter activity dye assay was performed by using fluorescent model substrate rhodamine B (RB) in combination with inhibitors of MXR efflux pumps: MK571 and Verapamil, known to inhibit multidrug resistance-associated proteins (MRP) and P-glycoprotein (P-gp), respectively. In this assay, the increase in intracellular fluorescence of the substrate dye, indicates inhibition of MXR efflux protein pumps. The assay was performed in three different tissues (gills, hepatopancreas, tail muscle). Additionally, tissues were exposed to selected heavy metals – mercury (HgCl2) and zinc (ZnCl2), known to occur in open freshwaters as pollutants. Results: Optimal time for RB accumulation in gills and hepatopancreas was determined to be 30 minutes. RB efflux in gills was inhibited by MK571 and in hepatopancreas by Verapamil, suggesting that multidrug resistanceassociated proteins are dominant in gills of P. leniusculus, and P-glycoprotein in hepatopancreas. Finally, inhibitory effect of mercury (HgCl2: 10 and 20 μM) and zinc (ZnCl2: 5–20 μM) on multixenobiotic resistance mechanism activity in gills, and only mercury in hepatopancreas, was detected. Conclusions: The results for the first time demonstrate the presence of multixenobiotic resistance mechanism efflux activity as an important tissue specific defense mechanism in P. leniusculus and provide the basis for future molecular and toxicological studies of this invasive and adaptable species

SAVREMENE TEHNIKE ANALIZE PODATAKA ZA MENADŽMENT ONLAJN REPUTACIJE U HOTELIJERSTVU I TURIZMU

Knowing what attracts or deters tourists to/from a tourist visit and what products to offer them and to pay special attention to is crucial for good economic results. Such knowledge can be obtained by analysis of online comments and reviews that tourists leave on travel websites (such as Booking, TripAdvisor, Trivago, etc.). This paper describes the value which information about opinions and emotions hidden in online reviews has for managers who receive it, especially the knowledge of (dis)satisfaction of users with certain aspects of the tourist offer. Uncovered knowledge from online reviews provides a chance to take advantage of the strong points, and correct the shortcomings through timely corrective measures and actions. Contemporary approaches and methods of analyzing online reviews and the opportunities for development they provide in the tourism industry are described through a case study conducted over a subset of 20491 hotel reviews from TripAdvisor. We have conducted sentiment analysis of reviews with the goal of building an automated model which will successfully distinguish positive from negative reviews. Logistic Regression classifier has the best performance, in 90% of reviews it has correctly classified positive reviews and in 83% negative. We have illustrated how association rules can help management to uncover relationships between concepts under discussion in negative and positive reviews.Saznanja o tome šta privlači a šta odvraća turiste od turističke posete i na koje proizvode obratiti posebnu pažnju, te koje proizvode ponuditi je od presudne važnosti za ostvarivanje dobrih ekonomskih rezultata. Do saznanja ove vrste možemo doći analizom onlajn komentara i recenzija koje savremeni turisti ostavljaju nakon turističkog iskustva na veb sajtovim (kao što su Booking.com, TripAdvisor, Trivago, i dr.). U radu je opisan značaj onlajn recenzija za menadžment, koji putem njih dobija informaciju o mišljenjima i emocijama korisnika njihovih turističkih usluga, a pogotovu o (ne)zadovoljstvu određenim aspektima ponude, te se pruža mogućnost da iskoriste uočene prednosti, a isprave nedostatke preduzimanjem pravovremenih korektivnih mera i akcija. Kroz studiju slučaja nad 20491 recenzijom sa TripAdvisor-a su opisani savremeni pristupi i metode za analizu korisnički generisanog sadržaja i mogućnosti za unapređenje koje one donose u domenu hotelijerstva i turizma. Realizovana je sentiment analiza nad prikupljenim onlajn recenzijama sa ciljem izgradnje automatizovanog modela koji uspešno pravi razliku između pozitivnih i negativnih recenzija. Klasifikacioni model zasnovan na logističkoj regresiji ispoljava najbolje performanse. U 90% slučajeva uspešno klasifikuje pozitivne recenzije, dok u 83% slučajeva uspešno klasifikuje negativne. Pored primene sentiment analize, ilustrovana je upotreba asocijativnih pravila kao pomoć menadžmentu u otkrivanju relacija između koncepata o kojima posetioci diskutuju unutar pozitivnih, odnosno negativnih recenzija