Proceedings of the twenty-first international conference on Machine learning

Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning.

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Semi-supervised learning: a brief review

During the last decade, a large number of different definitions and taxonomies of Internet of Things (IoT) systems have been proposed. This has resulted in a fragmented picture and a lack of consensus about IoT systems and their constituents. To provide a better understanding of this issue and a way forward, we have conducted a Systematic Mapping Study (SMS) of existing IoT System taxonomies. In addition, we propose a characterization of IoT systems synthesized from the existing taxonomies, which provides a more holistic view of IoT systems than previous taxonomies. It includes seventeen characteristics, divided into two groups: elements and quality aspects. Finally, by analyzing the results of the SMS, we draw future research directions.

Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study

Geo-distributed Data Centers (DCs) are increasingly common in order to provide scalability for increasing compute demands of modern applications When multiple geo-distributed DCs serve user requests, it is important to determine which DC and server to select to fulfill the demand at minimum cost, given that enough resources are available in terms of eg, CPU and bandwidth This is a complex task since every DC has different operational costs due to eg energy, carbon emission, and bandwidth costs In this paper, we develop a novel mathematical optimization model that guides the decision maker which DC to select, which server to use, and which DC gateway and network path to use to route the user demand in order to satisfy the time varying compute, bandwidth, and latency demands Our model is based on the concept of virtual networks, which have different requirements in terms of eg latency, and we model the queuing delay as a function of the traffic load Our extensive numerical evaluation, which is based on real-world DC locations, their resource provision costs, and typical demand patterns, shows how operational costs increase with the traffic load, and we analyze the impact of different latency bounds on the performance of different virtual networks

Cost-efficient resource scheduling under QoS constraints for geo-distributed data centers

A clustering fuzzification algorithm based on ALM

This paper proposes an efficient algorithm for contrast enhancement to preserve the essential details of microscopic images of malaria infected blood using Gamma Equalization (GE). The central idea of this method is first, to convert the input color blood image into gray scale one, and then to calculate the range value for the γth order image of a gray scale image. The look-up-table (LUT) values are calculated and the gray scale image pixel intensity values are converted into LUT values which yield final contrast-enhanced image by retaining the essential details. We tested different values of gamma (γ). The value of γ = 0.8 yields maximum contrast enhanced image, which is very useful for image analysis and a computer aided diagnostic system for malaria. On comparison, GE is found to be better than Histogram equalization (HE), Imadjust (IA) and Contrast-limited adaptive histogram equalization (CLAHE) for microscopic blood images of malaria by using image quality measures: Absolute mean Brightness error (AMBE), Entropy and average luminance.

Contrast-enhanced microscopic imaging of Malaria parasites

The single-link clustering method can find arbitrary shaped clusters. Single-link based semi-supervised clustering method in the presence of a few labeled data and constraints is presented in the paper. Based on the labeled data or constraints, the dissimilarity function is modified. Merging of clusters is done, as in the conventional singlelink method. The "must-link" constraints can easily be integrated in to the process, but "can-not link" constraints poses its own problems. Earlier attempts to overcome this problem, suggested the usage of the complete-link clustering method in place of the single-link method and the method thus derived is called the constrained complete-link (CCL) method. But, with a lesser number of constraints, the CCL method's working is very similar to that of the conventional complete-link method which cannot find arbitrary shaped clusters. The proposed semi-supervised singlelink (SSL) method can overcome this, also it can overcome the "noisy bridge" problem which is a well known problem present with the single-link method. The SSL method does not use the precomputed distance matrix alone, as the CCL method does, but, distance between clusters is computed by taking in to account both pre-computed distance matrix and constraints. Experimental studies are done using both standard and synthetic data sets. It is shown that the proposed SSL method is consistently superior than the CCL method and other conventional linkage based methods.

Semi-supervised single-link clustering method

Breast cancer is one of the major causes of cancer deaths in women. CAD (Computer Aided Detection) is becoming an integral part and helping radiologists to detect cancer as early as possible. Different CAD solutions have been devised, but we require a system that provides accurate detection of relevant lesions. This paper uses a DE based Automatic Fuzzy Clustering (DEAFC) algorithm for detection of lesions from mammogram images. The algorithm is tested on DDSM, MIAS and identifies the lesions. The performance is compared with the ground truth values which are manual markings of radiologist.

Detection of Lesion in Mammogram Images Using Differential Evolution Based Automatic Fuzzy Clustering

Cloud based services have become an integral part of our life. These services are based on infrastructure known as a data Center. As the demand for cloud based services increases the load on the data centers also increases and if this load is not properly managed the overall performance of the cloud degrades. This paper proposes a dynamic approach for load balancing a cloud that exploits the presence of datacenters globally. The proposed approach applies a strategy based on inter data center load migration. The approach illustrated in the paper tries to improve overall performance of the cloud by minimizing service delay.

Exploiting Geo Distributed datacenters of a cloud for load balancing

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

B. Eswara Reddy

Papers

Contrast-enhanced microscopic imaging of Malaria parasites

Semi-supervised single-link clustering method

Detection of Lesion in Mammogram Images Using Differential Evolution Based Automatic Fuzzy Clustering

Exploiting Geo Distributed datacenters of a cloud for load balancing

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation