Market-Based Approach to Mobile Surveillance Systems

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively

Molecular Characterization, Developmental Expression and Immunolocalization of Clathrin Heavy Chain in the Ovary of the American Cockroach, Periplaneta Americana During Oogenesis

Clathrin is the principal protein involved in receptor mediate endocytosis and the main component of the coated vesicles. It is composed of three identical clathrin heavy chains (CHC), each with an attached light chain. We characterized the deduced amino acid sequence of the partial cDNA clone of the American cockroach, Periplaneta americana (Pam) CHC. The analysis showed that this sequence is represented as multiple alpha helical repeats occurred in the arm region of the CHC and displayed a high level of identity and similarity to mosquitoes and Drosophila melanogaster CHCs. This is the first report on CHC from a hemimetabolous insect. The amplified CHC probe could hybridize two CHC transcripts in the current preparations, 6.3 kb and 7.3 kb. The Northern blot analysis confirmed that a 6.3 kb transcript is specifically expressed in ovarian tissues at high levels throughout the ovarian development, especially in previtellogenic ovaries (Days 1-4) but dropped during the vitellogenic period (days 5-7) and ultimately no transcript was detected in fully vitellogenic ovaries (days 9-13). Immunoblot analysis detected an ovary specific CHC protein of ~175 kDa that was present in previtellogenic ovaries on the day of female emergence and after initiation of vitellogenesis and onset of Vg uptake. Immunocytochemistry localized CHC protein to germ-line derived cells, oocytes, and revealed that CHC translation begins very early during oocyte differentiation in the germarium. The present work suggested a possible role for clathrin in the early fluid phase endocytosis (pinocytosis) in addition to its role in receptor-mediated endocytosis

Molecular characterization, developmental expression and immunolocalization of clathrin heavy chain in the ovary of the American cockroach, Periplaneta americana during oogenesis

Clathrin is the principal protein involved in receptor mediate endocytosis and the main component of the coated vesicles. It is composed of three identical clathrin heavy chains (CHC), each with an attached light chain. We characterized the deduced amino acid sequence of the partial cDNA clone of the American cockroach, Periplaneta americana (Pam) CHC. The analysis showed that this sequence is represented as multiple alpha helical repeats occurred in the arm region of the CHC and displayed a high level of identity and similarity to mosquitoes and Drosophila melanogaster CHCs. This is the first report on CHC from a hemimetabolous insect. The amplified CHC probe could hybridize two CHC transcripts in the current preparations, 6.3 kb and 7.3 kb. The Northern blot analysis confirmed that a 6.3 kb transcript is specifically expressed in ovarian tissues at high levels throughout the ovarian development, especially in previtellogenic ovaries (Days 1-4) but dropped during the vitellogenic period (days 5-7) and ultimately no transcript was detected in fully vitellogenic ovaries (days 9-13). Immunoblot analysis detected an ovary specific CHC protein of ~175 kDa that was present in previtellogenic ovaries on the day of female emergence and after initiation of vitellogenesis and onset of Vg uptake. Immunocytochemistry localized CHC protein to germ-line derived cells, oocytes, and revealed that CHC translation begins very early during oocyte differentiation in the germarium. The present work suggested a possible role for clathrin in the early fluid phase endocytosis (pinocytosis) in addition to its role in receptor-mediated endocytosis

OFCOD: On the Fly Clustering Based Outlier Detection Framework

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics

Rough – Granular Computing knowledge discovery models

Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology

Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics