Measuring the Utilization of On-Page Search Engine Optimization in Selected Domain

Search engine optimization (SEO) techniques involve „on-page“ and „off-page“ actions taken by web developers and SEO specialists with aim to increase the ranking of web pages in search engine results pages (SERP) by following recommendations from major search engine companies. In this paper we explore the possibility of creating a metric for evaluating on-page SEO of a website. A novel „k-rank“ metric is proposed which takes into account not only the presence of certain tags in HTML of a page, but how those tags are used with selected keywords in selected domain. The „k-rank“ is tested in domain of education by inspecting 20 university websites and comparing them with expert scores. The overview of results showed that „k-rank“ can be used as a metric for on-page SEO

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages

Web page content adjustment for search engines using machine learning and natural language processing

Optimizacija mrežnih stranica za tražilice (engl. Search engine optimization, SEO) podrazumijeva tehnike pomoću kojih autor mrežnih stranica provodi nad svojim stranicama kako bi one što bolje rangirale u organskim (prirodnim) rezultatima pretraživanja na internetskim tražilicama za odabrane ključne riječi. Taj proces između ostalog uključuje i optimizaciju sadržaja, odnosno prilagodbu sadržaja mrežnih stranica prema preporukama za optimizaciju mrežnih stranica za tražilice (u daljem tekstu SEO preporukama). Ovim istraživanjem ispituje se mogućnost upotrebe strojnog učenja za klasifikaciju mrežnih stranica u tri predefinirane klase s obzirom na stupanj prilagodbe sadržaja SEO preporukama. Pomoću strojnoga učenja izgrađeni su klasifikatori koji su naučili svrstati nepoznati uzorak (mrežnu stranicu) u predefinirane klase, te utvrditi značajne faktore (varijable) koje utječu na stupanj prilagodbe. Također izgrađen je sustav ispravka „neprilagođenih“ stranica upotrebom tehnika iz domene obrade prirodnog jezika. Rezultati su pokazali da se pomoću strojnog učenja može ocijeniti stupanj prilagođenosti stranice SEO preporukama, da se strojno učenje može koristiti za utvrđivanje značajnih faktora, te da se izgrađeni sustav prilagodbe može koristiti za ispravak tj. poboljšanje mrežnih stranica koje su u prethodnim fazama klasificirane kao "neprilagođene".Search engine optimization (SEO) involves techniques by which the author of the website customizes the website so that it ranks higher in organic (natural) search results on popular Internet search engines for selected keywords. This process includes, among others, the optimization of content (text) to fit SEO recommendations. This study examines the possibility of using machine learning tecniques to classify web pages into three predefined classes related to the degree of content adjustment to the SEO recommendations. Using machine learning algorithms, classifiers are built and trained to classify an unknown sample (web page) in the predefined classes and to identify important factors that affect the degree of adjustment. In addition, using algorithms from the domain of natural language processing a system for correction is built and tested. Results show that machine learning can be used to predict the degree of adjustments of web pages to SEO recommendations, for identifying important SEO factors and that the proposed correction system can be used to correct pages which were classified as "misfits" in prior stages

Mogu li faktori rizika biti bolji prediktori rane akutne mezenterijalne ishemije od laboratorijskih i slikovnih pretraga? Retrospektivna studija i algoritam za ranu intervenciju

Objectives The aim is to delineate relevant risk factors and construct an algorithm for earlier performance of selective mesenteric angiography, thus lowering mortality rates of acute mesenteric ischemia. Methods During a 5-year period 31 patients were examined. Thirteen risk factors were analysed and compared to standard diagnostic procedures. Results Only one patient did not have arterial hypertension. The second most common risk factor is atrial fibrillation with the incidence rate of 64.5%. The largest group of patients (38.7%) had two risk factors and there were 6.5% patients with six risk factors and 87.2% of all patients had two or more risk factors. In 67.7% of the patients we only performed emergency laparotomy due to inoperable state. Hospital mortality was 74.2%. Conclusions Combination of age over 70, hypertension and two or more risk factors associated with elevated D-dimers, in a patient with severe abdominal pain and minimal clinical findings with nonspecific laboratory findings and plain abdominal radiographs, could be an indication for early selective mesenteric angiography.Ciljevi Cilj je naznačiti relevantne faktore rizika i izraditi algoritam za ranije slučajeve selektivne mezenterijalne angiografije te na taj način smanjiti stopu smrtnosti akutne mezenterijalne ishemije. Metode Tijekom petogodišnjeg razdoblja pregledan je 31 pacijent. Analizirano je 13 faktora rizika koji su uspoređeni sa standardnim dijagnostičkim postupcima. Rezultati Samo jedan pacijent nije imao arterijsku hipertenziju. Drugi najčešći faktor rizika je atrijalna fibrilacija s učestalošću od 64,5%. Najveća skupina pacijenata (38,7%) imala je dva faktora rizika, 6,5% pacijenata imalo je šest faktora rizika, a 87,2% svih pacijenata imalo je dva ili više faktora rizika. U 67,7% pacijenata izveli smo samo hitnu laparotomiju zbog neoperabilnog stanja. Bolnička smrtnost bila je 74,2%. Zaključci Kombinacija dobi iznad 70, hipertenzije i dva ili više faktora rizika povezanih s povišenim D-dimerima, kod pacijenta sa znatnom abdominalnom boli i minimalnim kliničkim zaključcima s nespecifičnim laboratorijskim zaključcima i uobičajenim abdominalnim radiografima mogu biti indikacija za ranu selektivnu mezenterijalnu angiografiju

Tumor retroperitoneuma: pričaj mi o živcima Retroperitonealni femoralni schwannom sa jatrogenom postoperacijskom femoralnom neuropatijom: prikaz slučaja

Retroperitoneal femoral schwannomas constitute a rather small percentage of primary retroperitoneal tumors. Proper preoperative diagnosis is often difficult since imaging studies are nonspecific and differential diagnosis quite extensive. We present the case of a 71-year-old patient with a radiologically described retroperitoneal mass - postoperatively confirmed by pathohistology as a benign schwannoma. The tumor was removed in toto; however, the postoperative course was complicated with symptoms of femoral nerve damage. Although benign in nature (and exceedingly rare to turn malignant) schwannomas are treated surgically as the rate of complete resection without nerve damage is high. Left untreated they gain in mass and can cause significant pain due to displacement of the involved nerve. The significance of this case report is in highlighting the importance of considering schwannomas as a differential diagnosis of retroperitoneal tumors which in turn will lead to a strong strategy for avoiding postoperative complications.Retroperitonealni schwannomi femoralnog živca predstavljaju mali udio primarnih tumora retroperitoneuma. Uzimajući u obzir kako je radiološka dijagnostika u ovom slučaju nespecifična, a diferencijalna dijagnoza primarnih retroperitonealnih tumora široka, ispravnu preoperacijsku dijagnozu ponekad je teško postići. U ovom radu prikazat ćemo slučaj 71-godišnjeg pacijenta sa radiološki opisanim tumorom retroperitoneuma koji je nakon kirurške ekstirpacije patohistološki definiran kao schwannom. Tumor je odstranjen u cijelosti, međutim postoperacijski tijek kompliciran je simptomima jatrogenog oštećenja femoralnog živca. Iako su schwannomi uglavnom dobroćudni tumori uz rijetke slučajeve maligne alteracije, njihovo liječenje prvenstveno je kirurško. Postotak cjelovite resekcije bez oštećenja živca je visok. Neliječeni schwanomi dobivaju na masi te uzrokuju bol pomicanjem i pritiskanjem okolnih struktura. Važnost ovog prikaza leži u naglašavanju femoralnog schwannoma kao diferencijalno-dijagnostičke mogućnosti u razmatranju retroperitonealnih tumora. Na taj način može se stvoriti uspješna strategija sprječavanja postoperacijskih komplikacija

Immunohistochemical expression of 8-oxo-7,8-dihydro-2′-deoxyguanosine in cytoplasm of tumour and adjacent normal mucosa cells in patients with colorectal cancer

BACKGROUND: The aim of this research was to study the levels of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) in tumour tissue samples of colorectal carcinoma based upon immunohistochemical detection and compare those results with patients' outcome. ----- METHODS: Tumour blocks of patients surgically treated for colorectal cancer were evaluated by 8-oxodG immunohistochemical staining. The expression was analysed in 500 tumour cells. The percentage of positive cells, as well as staining intensity, was recorded, and Allred score was calculated. For each patient, data of age, gender, tumour size and location, margin status, histologic grade, tumour stage, lymph node status, vascular invasion, overall survival, and therapy protocols were collected. Tumour grade was divided into two groups as low and high grade. ----- RESULTS: In this study, 146 consecutive patients with primary colorectal carcinoma were included. All data were available for 138 patients, and they were included in this research. There were 83 male and 55 female patients; the median age was 64 years (range 35-87 years). The results showed shorter 5- and 10-year survival in patients with 8-oxodG positive tumour cells (5-year survival, n=138, Mantel-Cox, chi-square 4.116, degree of freedom (df)=1, p<0.05; 10-year survival, n=134, Mantel-Cox, chi-square 4.374, df=1, p<0.05). The results showed a positive correlation between Allred score and high tumour grade (two-tailed Spearman's ρ 0.184; p<0.05), as well as with non-polypoid tumour growth (two-tailed Spearman's ρ 0.198; p<0.05). There was no significant difference of 8-oxodG expression related to age, sex, blood group, size and tumour site, distance from the edge of the resected tumour margin, lymph nodes involvement, and vascular invasion. ----- CONCLUSIONS: In this study, the positive correlation between 8-oxodG presence in the tumour cells, worse clinical outcome, higher tumour grade, and flat morphology was found

Hyperthermic Intraperitoneal Chemotherapy (HIPEC) and Cytoreductive Surgery (CS) as Treatment of Peritoneal Carcinomatosis: Preliminary Results in Croatia

The purpose of our study was to evaluate initial results following introduction of Hyperthermic Intraperitoneal Chemotherapy (HIPEC) and Cytoreductive Surgery (CS). Twenty two patients with intraperitoneal malignancy undergone cytoreductive surgery (CS) and hyperthermic intraoperative chemotherapy (HIPEC) between January of 2007 and January 2010. Nine patients had adenocarcinoma of colorectal origin, 8 patients had ovarian cancer, and 5 had pseudomyxoma peritonei. Inclusion criteria were diagnosis of peritoneal carcinomatosis based on intraoperative assessment during first operative procedure for intraabdominal malignancy or follow-up diagnostic imaging proof. Excluded were patients with known malignant proliferation outside abdomen, liver metastasis and ASA score 4 and higher. All patients with pseudomyxoma peritonei diagnosis are alive, with mean follow-up time 24.8 months (range 15–35). In group of patients with adenocarcinoma from colorectal origin, 3 died, resulting in mean survival time 7.6 months (range 1–16). In group of patients with ovarian cancer, 2 died, resulting in mean survival time 13.8 months (range 0–31). Two patients died in early postoperative period. Most of the patients had some sort of mental disorder. Although HIPEC with CS improves survival, during introduction period higher morbidity and mortality could be expected

Hyperthermic Intraperitoneal Chemotherapy (HIPEC) and Cytoreductive Surgery (CS) as Treatment of Peritoneal Carcinomatosis: Preliminary Results in Croatia

The purpose of our study was to evaluate initial results following introduction of Hyperthermic Intraperitoneal Chemotherapy (HIPEC) and Cytoreductive Surgery (CS). Twenty two patients with intraperitoneal malignancy undergone cytoreductive surgery (CS) and hyperthermic intraoperative chemotherapy (HIPEC) between January of 2007 and January 2010. Nine patients had adenocarcinoma of colorectal origin, 8 patients had ovarian cancer, and 5 had pseudomyxoma peritonei. Inclusion criteria were diagnosis of peritoneal carcinomatosis based on intraoperative assessment during first operative procedure for intraabdominal malignancy or follow-up diagnostic imaging proof. Excluded were patients with known malignant proliferation outside abdomen, liver metastasis and ASA score 4 and higher. All patients with pseudomyxoma peritonei diagnosis are alive, with mean follow-up time 24.8 months (range 15–35). In group of patients with adenocarcinoma from colorectal origin, 3 died, resulting in mean survival time 7.6 months (range 1–16). In group of patients with ovarian cancer, 2 died, resulting in mean survival time 13.8 months (range 0–31). Two patients died in early postoperative period. Most of the patients had some sort of mental disorder. Although HIPEC with CS improves survival, during introduction period higher morbidity and mortality could be expected

Radiofrequency ablation as locoregional therapy for unresectable hepatic malignancies: initial results in 24 patients with 5-years follow-up

Radiofrequency ablation (RFA) is one treatment modality for unresectable liver metastases. Patients with hepatic malignancies (n = 24) underwent elective RFA. All tumors were ablated with a curative intent, with a margin of 1 cm, in a single session of RFA. The median diameter of tumor was 3.1 cm (range 1.7-6.9 cm). Studied patients were not candidates for resection due to multifocal hepatic disease, extrahepatic disease, proximity to major vascular structures or presence of cirrhosis with functional hepatic reserve inadequate to tolerate major hepatic resection. Complete tumor necrosis was achieved in 87.5% and tumor recurred in 3 patients (12.5%) with lesions larger than 5 cm. Distant intrahepatic recurrence was diagnosed in another 4 (16.7%). Distant metastases were found in 7 (29.2%) patients. Four of these 7 patients had also distant intrahepatic recurrence of disease. Two and 5-years survival rates were 41.7% (10 patients) and 8.3% (2 patients) respectively. RFA is safe and effective option for patients with unresectable hepatic malignancies smaller than 5 cm without distant metastatic disease. RF ablation resulted in complete tumor necrosis in 87.5% with 2 and 5-years survival rates much higher than with chemotherapy alone or only supportive therapy, when survival is measured in weeks or months. If RFA is unavailable, percutaneous ethanol injection therapy can be done but with inferior survival rates