Conference Papers
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Item Metaheuristic for Optimize the India Speed Post Facility Layout Design and Operational Performance Based Sorting Layout Selection Using DEA Method(Springer Verlag service@springer.de, 2020) Vadivel, S.M.; Sequeira, A.H.; Jauhar, S.K.Adoption of feasible location science is gaining more interest in the field of Facility Layout Design (FLD) problems among working researchers group. Many methods such as MCDM, Heuristics and Intelligent approaches are available to solve the FLD problems. However in reality, finding the feasible facility layout selection is subject to management as well as performances oriented selections. Here in India, speed post mail processing service industry is facing tremendous challenges like tumbling demands due to low production concern, gloomy trend in technology advancement, and fierce private couriers’ competition. Hence, the highly competitive operational performance is of much concern and attention is focused towards the direction of facility location science. This paper aims to examine the challenges of sustainable operational performance oriented layout selection by Data Envelopment Analysis (DEA) and proposes a genetic algorithm (GA) related to intelligent based approach, for finding the optimal total facility layout cost for a hypothetical South Indian speed post service office layout. In this paper, we used multiple-criteria facility layout selection problem using mathematical model generated with Data Envelopment Analysis (DEA). © 2020, Springer Nature Switzerland AG.Item On Human Identification Using Running Patterns: A Straightforward Approach(Springer Verlag service@springer.de, 2020) Anusha, R.; Jaidhar, C.D.Gait is a promising biometric for which various methods have been developed to recognize individuals by the pattern of their walking. Nevertheless, the possibility of identifying individuals by using their running video remains largely unexplored. This paper proposes a new and simple method that extends the feature based approach to recognize people by the way they run. In this work, 12 features were extracted from each image of a gait cycle. These are statistical, texture based and area based features. The Relief feature selection method is employed to select the most relevant features. These selected features are classified using k-NN (k-Nearest Neighbor) classifier. The experiments are carried out on KTH and Weizmann database. The obtained experimental results demonstrate the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.Item Towards an Upper Ontology and Hybrid Ontology Matching for Pervasive Environments(Springer Verlag service@springer.de, 2020) Karthik, N.; Ananthanarayana, V.S.Pervasive environments include sensors, actuators, handheld devices, set of protocols and services. The specialty of this environment is its power to manage with any device at any time anywhere and work autonomously for providing customized services to user. The different entities of pervasive environment collaborate with each other to accomplish an objective by sharing data among them. It raises an interesting problem called semantic heterogeneity. To address this problem, a hybrid ontology matching technique which combines direct and indirect matching techniques is proposed in this paper. To share and integrate data semantically, ontology matching technique establishes a semantic correspondence among various entities of pervasive application ontologies. To find the efficiency of proposed approach, we carried out set of experiments with real world pervasive applications. Experimental results prove that the proposed approach shows excellent performance in hybrid ontology matching. Results also proved that the use of background knowledge has influence over the performance of ontology matching technique. © 2020, Springer Nature Switzerland AG.Item Intrinsic evaluation for english–tamil bilingual word embeddings(Springer Verlag service@springer.de, 2020) Jp, J.P.; Krishna Menon, V.K.; Rajendran, S.; Padannayil, K.P.; Anand Kumar, M.A.Despite the growth of bilingual word embeddings, there is no work done so far, for directly evaluating them for English–Tamil language pair. In this paper, we present a data resource and evaluation for the English–Tamil bilingual word vector model. In this paper, we present dataset and the evaluation paradigm for English–Tamil bilingual language pair. This dataset contains words that covers a range of concepts that occur in natural language. The dataset is scored based on the similarity rather than association or relatedness. Hence, the word pairs that are associated but not literally similar have a low rating. The measures are quantified further to ensure consistency in the dataset, mimicking the cognitive phenomena. Henceforth, the dataset can be used by non-native speakers, with minimal effort. We also present some inferences and insights into the semantics captured by word vectors and human cognition. © Springer Nature Singapore Pte Ltd. 2020.Item DROCC: Deep Robust One-Class Classification(ML Research Press, 2020) Goyal, S.; Raghunathan, A.; Jain, M.; Simhadri, H.; Jain, P.Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images. State-of-the-art methods aim to leverage deep learning to learn appropriate features via two main approaches. The first approach based on predicting transformations (Golan & El-Yaniv, 2018; Hendrycks et al., 2019a) while successful in some domains, crucially depends on an appropriate domain-specific set of transformations that are hard to obtain in general. The second approach of minimizing a classical one-class loss on the learned final layer representations, e.g., DeepSVDD (Ruff et al., 2018) suffers from the fundamental drawback of representation collapse. In this work, we propose Deep Robust One Class Classification (DROCC) that is both applicable to most standard domains without requiring any side-information and robust to representation collapse. DROCC is based on the assumption that the points from the class of interest lie on a well-sampled, locally linear low dimensional manifold. Empirical evaluation demonstrates that DROCC is highly effective in two different one-class problem settings and on a range of real-world datasets across different domains: tabular data, images (CIFAR and ImageNet), audio, and time-series, offering up to 20% increase in accuracy over the state-of-the-art in anomaly detection. Code is available at https://github.com/microsoft/EdgeML. © 2020 by the author(s).Item Dynamic mode-based feature with random mapping for sentiment analysis(Springer Verlag service@springer.de, 2020) Sachin Kumar, S.; Anand Kumar, M.A.; Padannayil, K.P.; Poornachandran, P.Sentiment analysis (SA) or polarity identification is a research topic which receives considerable number of attention. The work in this research attempts to explore the sentiments or opinions in text data related to any event, politics, movies, product reviews, sports, etc. The present article discusses the use of dynamic modes from dynamic mode decomposition (DMD) method with random mapping for sentiment classification. Random mapping is performed using random kitchen sink (RKS) method. The present work aims to explore the use of dynamic modes as the feature for sentiment classification task. In order to conduct the experiment and analysis, the dataset used consists of tweets from SAIL 2015 shared task (tweets in Tamil, Bengali, Hindi) and Malayalam languages. The dataset for Malayalam is prepared by us for the work. The evaluations are performed using accuracy, F1-score, recall, and precision. It is observed from the evaluations that the proposed approach provides competing result. © Springer Nature Singapore Pte Ltd. 2020.Item Feature selection using fast ensemble learning for network intrusion detection(Springer Verlag service@springer.de, 2020) Pasupulety, U.; Adwaith, C.D.; Hegde, S.; Patil, N.Network security plays a critical role in today’s digital system infrastructure. Everyday, there are hundreds of cases of data theft or loss due to the system’s integrity being compromised. The root cause of this issue is the lack of systems in place which are able to foresee the advent of such attacks. Network Intrusion detection techniques are important to prevent any system or network from malicious behavior. By analyzing a dataset with features summarizing the method in which connections are made to the network, any attempt to access it can be classified as malicious or benign. To improve the accuracy of network intrusion detection, various machine learning algorithms and optimization techniques are used. Feature selection helps in finding important attributes in the dataset which have a significant effect on the final classification. This results in the reduction of the size of the dataset, thereby simplifying the task of classification. In this work, we propose using multiple techniques as an ensemble for feature selection. To reduce training time and retain accuracy, the important features of a subset of the KDD Network Intrusion detection dataset were analyzed using this ensemble learning technique. Out of 41 possible features for network intrusion, it was found that host-based statistical features of network flow play an import role in predicting network intrusion. Our proposed methodology provides multiple levels of overall selected features, correlated to the number of individual feature selection techniques that selected them. At the highest level of selected features, our experiments yielded a 6% increase in intrusion detection accuracy, an 81% decrease in dataset size and a 5.4× decrease in runtime using a Multinomial Naive Bayes classifier on the original dataset. © Springer Nature Switzerland AG 2020.Item A novel approach for operational performance based mail sorting facility layout selection using grey relational analysis: A case on India speed post service industry(Springer Verlag service@springer.de, 2020) Vadivel, S.M.; Sequeira, A.H.Facility layout design (FLD) comes under multi-objective problem which has a major impact on service or manufacturing industries performance. Facility layout design purpose is to streamline the work flow smoothly and increasing the production by reducing the material handling time. An attempt has been done on Grey Relational Analysis (GRA) in order to identify the feasible layout for the aids of operational performance. This FLD focuses on finding and eliminating the waste in the process flow and brings the comfortable workplace environment in order to attain the operational performance. The recommended approaches are demonstrated through practical applications in India speed post sorting facility layouts. Practical results are promising for incorporating optimal method for solving layout design problem. © Springer Nature Switzerland AG 2020.Item Development of low-cost real-time driver drowsiness detection system using eye centre tracking and dynamic thresholding(Springer Verlag service@springer.de, 2020) Khan, F.; Sharma, S.One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the concentration and vigilance of the driver thereby increasing the risk of inherent human error leading to injuries and fatalities. Hence, our primary motive being - to reduce road accidents using a non-intrusive image processing based alert system. In this regard, we have built a system that detects driver drowsiness by real time tracking and monitoring the pattern of the driver’s eyes. The stand alone system consists of 3 interconnected components - a processor, a camera and an alarm. After initial facial detection, the eyes are located, extracted and continuously monitored to check whether they are open or closed on the basis of a pixel-by-pixel method. When the eyes are seen to be closed for a certain amount of time, drowsiness is said to be detected and an alarm is issued accordingly to alert the driver and hence, prevent a casualty. © Springer Nature Switzerland AG 2020.Item Mobility aware routing protocol based on DIO message for low power and lossy networks(Springer Verlag service@springer.de, 2020) Sanshi, S.; Jaidhar, C.D.Mobility support has become a crucial requirement for many of the Low power and Lossy Networks applications, and designing an efficient mobility aware routing protocol towards this end has become an important research topic. In this paper, a Mobility Aware Routing Protocol for Low power and Lossy Networks (MARPL) that updates the Preferred Parent Node information soon after it receives the DIO message has been proposed. In the proposed approach, the Mobile Node is aware of its mobility and updates the PPN information without waiting for the route expiration time. To measure the effectiveness of the proposed MARPL, simulation works were carried out on a Contiki-based Cooja simulator for different mobility models. The obtained simulation results showed that the proposed MARPL performed better compared with the standard RPL for healthcare applications. © Springer Nature Switzerland AG 2020.