A comparative study of selected classification accuracy in user profiling

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate

Investigation of Mobile IPv6 and SIP integrated architectures for IMS and VoIP applications

Mobile IPv6 and SIP are protocols designed to support different types of mobility. Mobile IPv6 has been used to support mobility in IP networks and SIP has been used for voice over IP applications. It is the signalling protocol of the IP multimedia subsystem (IMS). In this paper both protocols have been simulated and compared in order to observe their performance for voice over IP (VoIP) applications. In this paper the architectures proposed by researchers in order to combine mobile IPv6 and SIP have also been investigated and compared to analyse their advantages and disadvantages. A network scenario, running mobile IPv6 and SIP for IMS, has also been simulated in order to evaluate the performance offered by the two protocols and to compare them with the results from the simulation of the pure mobile IPv6 and SIP architectures. The comparison shows that the combined scenario offers better performance similar to the one obtained using only mobile IPv6 with route optimization. The scenario simulated was also compared with the integrated architectures for mobile IPv6 and SIP that were investigated

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs)

Lagrangian structure of flows in the Chesapeake Bay:Challenges and perspectives on the analysis of estuarine flows

In this work we discuss applications of Lagrangian techniques to study transport properties of flows generated by shallow water models of estuarine flows. We focus on the flow in the Chesapeake Bay generated by Quoddy (see Lynch and Werner, 1991), a finite-element (shallow water) model adopted to the bay by Gross et al. (2001). The main goal of this analysis is to outline the potential benefits of using Lagrangian tools for both understanding transport properties of such flows, and for validating the model output and identifying model deficiencies. We argue that the currently available 2-D Lagrangian tools, including the stable and unstable manifolds of hyperbolic trajectories and techniques exploiting 2-D finite-time Lyapunov exponent fields, are of limited use in the case of partially mixed estuarine flows. A further development and efficient implementation of three-dimensional Lagrangian techniques, as well as improvements in the shallow-water modelling of 3-D velocity fields, are required for reliable transport analysis in such flows. Some aspects of the 3-D trajectory structure in the Chesapeake Bay, based on the Quoddy output, are also discussed

In-vitro effects of biochemical factors on trypsin activity from intestine and pyloric caeca of common kilka (Clupeonella cultriventris caspia) for inhibition of belly bursting

The in-vitro effects of biochemical factors on trypsin activity from intestine and pyloric caeca of common kilka (Clupeonella cultriventris caspia) was evaluated. Trypsin is known to cause belly bursting in common kilka. The assessments showed that in a range of 4-70°C, trypsin from pyloric caeca and intestine of common kilka had the maximum activity and the satiability at 60 and 55°C, respectively. The pH assessments indicated that maximum activity and stability for trypsin were at 8.5 at pH range of 4-11. The effects of metal ions on trypsin activity revealed that CaCl2, MgCl2 and MnCl2 increased trypsin activity while CuCl2 ،ZnCl2 and Al2(SO4)3 decreased its activity. The effect of inhibitors on trypsin activity also showed that SBTI and TLCK (specific inhibitors for trypsin) significantly inhibited trypsin activity. This study suggests that belly bursting in common kilka can be prevented by trypsin inactivation through application of low temperature (4°C), acidic pH, metals of CuCl2, ZnCl2 and Al2(SO4)3 and inhibitors of SBTI and TLCK

Multi-level multi-criteria analysis of alternative fuels for waste collection vehicles in the United States

Historically, the U.S. waste collection fleet was dominated by diesel-fueled waste collection vehicles (WCVs); the growing need for sustainable waste collection has urged decision makers to incorporate economically efficient alternative fuels, while mitigating environmental impacts. The pros and cons of alternative fuels complicate the decisions making process, calling for a comprehensive study that assesses the multiple factors involved. Multi-criteria decision analysis (MCDA) methods allow decision makers to select the best alternatives with respect to selection criteria. In this study, two MCDA methods, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW), were used to rank fuel alternatives for the U.S. waste collection industry with respect to a multi-level environmental and financial decision matrix. The environmental criteria consisted of life-cycle emissions, tail-pipe emissions, water footprint (WFP), and power density, while the financial criteria comprised of vehicle cost, fuel price, fuel price stability, and fueling station availability. The overall analysis showed that conventional diesel is still the best option, followed by hydraulic-hybrid WCVs, landfill gas (LFG) sourced natural gas, fossil natural gas, and biodiesel. The elimination of the WFP and power density criteria from the environmental criteria ranked biodiesel 100 (BD100) as an environmentally better alternative compared to other fossil fuels (diesel and natural gas). This result showed that considering the WFP and power density as environmental criteria can make a difference in the decision process. The elimination of the fueling station and fuel price stability criteria from the decision matrix ranked fossil natural gas second after LFG-sourced natural gas. This scenario was found to represent the status quo of the waste collection industry. A sensitivity analysis for the status quo scenario showed the overall ranking of diesel and fossil natural gas to be more sensitive to changing fuel prices as compared to other alternatives

Perilaku Sosial Anak Putus Sekolah

Penelitian ini bertujuan untuk mengetahui perilaku sosial dan faktor penyebab anak putus sekolah di masyarakat Pattallassang Kabupaten Takalar. Penelitian yang dilaksanakan merupakan penelitian social budaya yang jenis penelitian yang digunakan adalah metode penelitian deskriptif kualitatif dengan cara penentuan sampel melalui teknik Purposive Sampling dengan memilih beberapa informan yang memiliki kriteria yang telah ditentukan oleh peneliti yakni mengetahui tentang Perilaku Sosial Anak Putus Sekolah. Dari hasil penelitian menunjukkan bahwa berdasarkan deskripsi hasil penelitian dan pembahasan menunjukkan bahwa faktor penyebab anak putus sekolah di masyarakat Pattallassang Kabupaten Takalar Secara umum adalah kondisi ekonomi keluarga yang kurang mendukung, factor lingkungan dan dari diri anak itu sendiri. Sementara perilaku sosial anak putus sekolah memperlihatkan bahwa perilakunya cenderung kepada hal-hal bersifat negatif, seperti: menjadi lebih nakal, sering keluar malam untuk berkumpul dengan teman-temannya, melakukan tindakan kekerasan, mabuk-mabukan, sampai mengkonsumsi narkoba. Namun, berbeda dengan anak putus sekolah kemudian melakukan aktivitas lain, seperti bekerja dan membantu orang tuanya mereka cenderung melakukan perilaku yang positif. Berbagai upaya juga dilakukan pemerintah setempat dalam mencegah terjadinya anak putus sekolah

Conjugation of quantum dots on carbon nanotubes for medical diagnosis and treatment

Cancer is one of the leading causes of death worldwide and early detection provides the best possible prognosis for cancer patients. Nanotechnology is the branch of engineering that deals with the manipulation of individual atoms and molecules. This area of science has the potential to help identify cancerous cells and to destroy them by various methods such as drug delivery or thermal treatment of cancer. Carbon nanotubes (CNT) and quantum dots (QDs) are the two nanoparticles, which have received considerable interest in view of their application for diagnosis and treatment of cancer. Fluorescent nanoparticles known as QDs are gaining momentum as imaging molecules with life science and clinical applications. Clinically they can be used for localization of cancer cells due to their nano size and ability to penetrate individual cancer cells and high-resolution imaging derived from their narrow emission bands compared with organic dyes. CNTs are of interest to the medical community due to their unique properties such as the ability to deliver drugs to a site of action or convert optical energy into thermal energy. By attaching antibodies that bind specifically to tumor cells, CNTs can navigate to malignant tumors. Once at the tumor site, the CNTs enter into the cancer cells by penetration or endocytosis, allowing drug release, and resulting in specific cancer cell death. Alternatively, CNTs can be exposed to near-infrared light in order to thermally destroy the cancer cells. The amphiphilic nature of CNTs allows them to penetrate the cell membrane and their large surface area (in the order of 2600 m2/g) allows drugs to be loaded into the tube and released once inside the cancer cell. Many research laboratories, including our own, are investigating the conjugation of QDs to CNTs to allow localization of the cancer cells in the patient, by imaging with QDs, and subsequent cell killing, via drug release or thermal treatment. This is an area of huge interest and future research and therapy will focus on the multimodality of nanoparticles. In this review, we seek to explore the biomedical applications of QDs conjugated to CNTs, with a particular emphasis on their use as therapeutic platforms in oncology

Functionalization of single-walled carbon nanotubes and their binding to cancer cells

Vide