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About this book

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Computing and Network Communications (CoCoNet'20), October 14–17, 2020, Chennai, India. The papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and Edge Computing, Distributed Systems, Machine Intelligence, Data Analytics, Cybersecurity, Artificial Intelligence and Cognitive Computing and Circuits and Systems. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains.

Table of Contents


Machine Learning, Visual Computing and Signal Processing


Computational Reconstructions of Extracellular Action Potentials and Local Field Potentials of a Rat Cerebellum Using Point Neurons

One of the main challenges in computational modeling of neurons is to reconstruct the realistic behavior of the neurons of the brain under different functional conditions. At the same time, simulation of large networks is time-consuming and requires huge computational power. The use of spiking neuron models could reduce the computational cost and time. In this study, the extracellular potentials were reconstructed from a single point neuron model of cerebellum granule neuron, and local field potentials (LFP) were modeled. Realistic reconstruction of cerebellum Crus II evoked post-synaptic local field potentials using simple models of granule neurons help to explore emergent behavior attributing patterns of information flow in the granule layer of the cerebellum. The modeling suggests that the evoked extracellular action potential (EAP) arises from the transmembrane currents correlating spiking activity and conductive properties of the extracellular medium to the LFP. The computation study reproduces experimentally observed in vitro N2a and N2b evoked LFP waves and can be used to test the scaling of models developed from a bottom-up approach.

Arathi Rajendran, Naveen Kumar Sargurunathan, Varadha Sasi Menon, Sneha Variyath, Satram Dayamai Sai, Shyam Diwakar

Iris Recognition Using Integer Wavelet Transform and Log Energy Entropy

Fernandez, Jincy J. Pandian, NithyanandamAs the technology is reaching its next level day by day, the concerns over information or data security are also creeping up. Biometric systems have been widely used in many real-world applications in order to provide more security to the data. Iris recognition system has become a widely used system for human identification from the last few decades. In this paper, an efficient iris recognition system is proposed where iris localization is carried out by first finding the pupil-iris boundary using the connected component analysis approach. And then by considering the pupil center as the reference point, it traverses through the virtual outer boundary to detect the iris-sclera boundary. After applying normalization on the iris region, the iris region is partitioned into non-overlapping blocks. Further, a combination of integer wavelet transform (IWT) with log energy entropy (LEE) is applied on each block to extract the unique iris code as the feature vector. The experiments have been conducted using the multimodal biometric database, SDUMLA-HMT. The proposed system has succeeded in achieving a low false acceptance rate and a very low false rejection rate. Also, the uniqueness of the iris patterns is evaluated in terms of degrees of freedom and is found to be a promising one.

Jincy J. Fernandez, Nithyanandam Pandian

Deep Learning-Based Approach for Skin Burn Detection with Multi-level Classification

Karthik, Jagannatha Nath, Gowrishankar S. Veena, A.In most recent years, convolutional neural network (CNN) model is the detail of craftsmanship form fruitful for photograph investigation. In this exploration, we are incorporating CNN models for classification of skin burn based on visual investigation. The aim of this paper is to develop a computerized mechanism in classifying the burn based on severity and compare the accuracies of various CNN algorithms for the same. Rapid development in deep learning enables automated learning of semantics, deep features that are easily learnt which addresses the problems of existing traditional image processing. The proposed method uses deep neural network, recurrent neural network and CNN model. The training is performed using dataset of 104 images classified into degree 1, degree 2 and degree 3 depending on the severity of the burn. Experimental analysis is also provided to compare the accuracies of different methods and identify the best model with better accuracy. The proposed computerized model can aid the medical experts in diagnosing the wound and suggest appropriate treatment depending on the severity of the skin burn. The proposed model could encourage telemedicine practise with the help of modern technology to remotely diagnose the patients especially in rural areas where there could be shortage of physicians.

Jagannatha Karthik, Gowrishankar S. Nath, A. Veena

Semantic Retrieval of Microbiome Information Based on Deep Learning

Pathogenic microorganisms are always a challenge when they form biofilms on submerged surfaces such as pipes, drains, or sewers, which are difficult to remove using normal chemical or biological treatments. Developing a fundamental understanding of the biodiversity of sewage microbiome or finding out the key species that can be targeted to significantly reduce the pathogenic population within can be critical in advancing and optimizing the technology for maintaining environmental health. Hence to find articles with relevant information about this microbiome and the interactions within is like finding a needle from the haystack. There comes the need for data mining tools, a key part of such a tool would be named entity recognition. To train a NER model, a relevant dataset with the required entities tagged is required and no such were to be found in the biomedical domain. So, in our study, we intended to develop a microbiome dataset with all the relevant concepts tagged for training a NER model which is to be a part of a semantic information retrieval tool. For this, we engineered a dataset specifically focusing on keywords related to the characteristics of the wastewater microbiome that could cluster out the relevant information from the bulk data of PubMed literature. The new engineered data was then used for fine-tuning NER models with different variants of BERT models for analyzing which had the most efficiency with our dataset. We implemented NER models capable of accurately predicting the concepts tagged in the microbiome dataset and designed experiments to validate the efficiency of the different models on our dataset and also other open-source biomedical datasets like JNLPA and BC5CDR. The results show that out of the three BERT variants, BioBERT was the most performant model, and also even with a fairly limited size compared to other biomedical NER datasets, we were able to achieve similar scores. The NER model fine-tuned using the microbiome dataset was able to successfully predict the tagged concepts/named entities in the datasets.

Joshy Alphonse, Anokha N. Binosh, Sneha Raj, Sanjay Pal, Nidheesh Melethadathil

Early Detection of COVID-19 from CT Scans Using Deep Learning Techniques

Limna Das, P. Manoj, A. Sai Sharma, Sachin Jayaraj, P. B.The novel coronavirus 2019 (COVID-2019) which began from China, further spread to all over the planet and was announced as a pandemic by WHO. It has blocked our daily lives and world economy to a large extent. In the lack of any particular vaccine for present pandemic COVID-19, it is necessary to recognize the disease at an early stage and quarantine these infected patients to stop the further spread. The popular diagnosis of COVID-19 is being done using polymerase chain reaction (PCR), but there are some cases of false interpretation. Rapid antibody test also has faulty/wrong implications. Till now, we have witnessed the global deficit of testing labs and testing kits for COVID-19. There is an urgent requirement for developing quick and reliable devices that can help doctors in diagnosing COVID-19. Developing a computer-based COVID detection tool will be very useful as it can screen the positive cases from a mass collection. Radiological imaging like computed tomography (CT) scans can be used for the early diagnosis. With the invention of AI algorithms, we can apply learning algorithms for early detection of COVID-19. 2016 on wards, deep learning, a deep neural network-based learning technique is widely applied in biomedical problems. In this article, we suggest a fast and reliable diagnostic tool using deep learning algorithms for identifying this pandemic. We have built two models for this purpose; one with an EfficientNet architecture using focal loss and a GradCam heatmap for testing its reliability in practical use. We also built a model using ResNet by custom vision AI of Microsoft Azure. Data was collected from different sources and the highly scaled EfficientNet architecture outperformed the Resnet architecture of MS Azure for classifying the COVID CT scans by an increase in accuracy of 10%. We are planning to deploy this software in the form of a chatbot. Also, our model continuously learns from data regularly and would attain better accuracy in future.

P. Limna Das, A. Sai Manoj, Sachin Sharma, P. B. Jayaraj

Towards Protein Tertiary Structure Prediction Using LSTM/BLSTM

Antony, Jisna Penikalapati, Akhil Reddy, J. Vinod Kumar Pournami, P. N. Jayaraj, P. B.Determining the native structure of a protein, given its primary sequence is one of the most demanding tasks in computational biology. Traditional protein structure prediction methods are laborious and involve vast conformation search space. Contrarily, deep learning is a rapidly evolving field with outstanding performance at problems where there are complicated relationships between input features and desired outputs. Various deep neural network architectures such as recurrent neural networks, convolution neural networks, deep feed-forward neural networks are becoming popular for solving problems in protein science. This work mainly concentrates on prediction of three-dimensional structure of proteins from the given primary sequences using deep learning techniques. Long short-term memory (LSTM) and bidirectional LSTM (BLSTM) neural network architectures are used for predicting protein tertiary structures from primary sequences. The result shows that single-layer BLSTM networks fed with primary sequence and position-specific scoring matrix data gives better accuracy compared to LSTM and two-layer BLSTM models. This study may get benefited to the computational biologists working in the area of protein structure prediction.

Jisna Antony, Akhil Penikalapati, J. Vinod Kumar Reddy, P. N. Pournami, P. B. Jayaraj

An Android-Based Smart Home Automation System in Native Language

Prakash, Nayan Thara Santhosh, Mathew Sneha Raj, M. P. Gokul, G. George, GeminiSpeech recognition is the ability of a machine to analyze and respond to the oration of a person. The proposed system mainly concentrates on the people who are visually disabled, paralyzed and handicapped, so that such users can monitor home appliances from anywhere inside a home. The project develops a home automation system which is connected with an Android smartphone by an Arduino device. In this advanced world, people want to switch from the conventional switches to centralized control system. Especially, when it comes to the elderly or handicapped people as mentioned earlier, they may feel trouble in managing all switches which are located at different parts of their residence. So, the system assists them in handling everything within their smartphones. Here the other strong point of the work is that when it comes to the people can use their native language to interact with the device, and hence, can cater to a larger unprivileged sections of the society.

Nayan Thara Prakash, Mathew Santhosh, M. P. Sneha Raj, G. Gokul, Gemini George

Live Acoustic Monitoring of Forests to Detect Illegal Logging and Animal Activity

Illegal cutting of trees and poaching in the forest has become a serious issue regarding environmental conservation. Trespassing in the forest has an adverse effect on the habitat of animals. There is no effective solution for real-time detection and warning of such activity. Image-based monitoring solutions are too costly and cannot cover a wide range of areas. A novel approach of audio-based monitoring systems using deep neural learning can be proposed as a solution to this problem. A model has to be trained using various audio samples of cutting of trees, gunshots, etc., along with the outliers. There are numerous tree felling techniques and hunting techniques. In the case of methods that are known to the model, the model detects that event and hence warns the authorities. The audio samples in the dataset in the time domain are converted to the frequency domain using fast Fourier transform (FFT). This distributes the signal across corresponding frequencies. For better visualization of features, it is then converted into a Mel scale, and the spectrum of this spectrum is computed using cosine transformation to obtain the Mel-frequency cepstral coefficients. Relevant features are then extracted using these coefficients and classify them using the proposed deep neural learning method. There is a significant difference between the energy concentration distributions of the sound that has to be detected with that of the outliers. This enables to classify the audio samples with a greater signal-to-noise ratio. The resulting model is then used for live monitoring of forests against illegal activities. The current situation of the wildlife demands an accurate database of animal activity in a particular area. This helps both the wildlife tourism and various studies. For addressing this issue, the proposed model is also trained to detect the presence of animals, and it will accomplish it without disturbing the wildlife activity.

J. C. Karthikeyan, S. Sreehari, Jithin Reji Koshy, K. V. Kavitha

CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing

Shetve, Dattaprasad VaraPrasad, Raja Trestian, Ramona Nguyen, Huan X. Venkataraman, HrishikeshIn the age of smart manufacturing, there are typically multitude of sensors that are connected to each assembly line. The amount of data generated could be used to create a digital twin model of the complete process; wherein virtual replicas of the device and the process can be created before and during the process. An important aspect is automatic anomaly detection in the manufacturing process. Anomaly/outlier detection identifies data-points, events and/or observations that deviate from the dataset’s normal behaviour. A major problem in predicting anomaly from datasets is the limited accuracy that can be achieved. Several state-of-the-art techniques provide very high accuracy (>95%). However, these result in a considerable increase in the required time, thereby limiting its use to non-real-time applications. This paper proposes a cluster-aided two-step (CATS) approach for anomaly detection wherein two unsupervised detection techniques are employed in serial. The technique used for the first step is density-based spatial clustering for applications with noise (DBSCAN), while the second technique is local outlier factor (LOF). The output of the first-step technique is fed to the second technique, thereby utilizing the knowledge generated in the first step. An extensive simulation analysis indicates that the proposed CATS algorithm results in >95% accuracy for the outlier population is above 15% with a prediction time of lesser than 85 s.

Dattaprasad Shetve, Raja VaraPrasad, Ramona Trestian, Huan X. Nguyen, Hrishikesh Venkataraman

Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction

Tata, Naveen Machiraju, Srivasthasva Srinivas Akshay, V. Menon, Divyasree Mohan Sai Shibu, N. B. Arjun, D.With the increase in the development of smart metering in energy systems, a large amount of data is being generated. The data consists of energy generated, energy consumed and energy stored with respect to time. This data can be used to improve the efficiency, reliability and stability of the power system by using machine learning algorithms. Energy requirement of each consumer can be predicted with the available data. Renewable energy generation can also be predicted. In this paper, different statistical and machine learning models are used to analyze the energy usage in smart communities. To validate the prediction models, smart meter data from our campus is used. The results show that the long short-term memory (LSTM) model is more suitable for energy demand prediction. The LSTM model is then used to predict the energy demand in students’ hostels during conditions such as climate and holidays.

Naveen Tata, Srivasthasva Srinivas Machiraju, V. Akshay, Divyasree Mohan Menon, N. B. Sai Shibu, D. Arjun

Ranking of Educational Institutions Based on User Priorities Using AHP-PROMETHEE Approach

Today, education plays a major role in bringing values to the coming generations. With right to education taken up seriously by the government, there is a vast increase in the number of students taking up various courses and degrees. To cater to these needs, several educational institutions have also come up with the intention of providing a variety of courses to the students. However, as the numbers increase, the need to assess these institutions also increases in order to help students decide the institution of their choice and also for the institutions to better themselves. To accomplish evaluation of such institutions, a number of ranking systems were established with a fixed set of criteria. However, a ranking system which would cater to the needs of a student individually was never developed. This paper aims to develop a ranking system, which ranks the educational institutions based on the needs of an individual, using analytical hierarchy process and the multi-criteria decision-making method PROMETHEE. In order to make this system automized, the data required for this process has been retrieved using the process of web crawling. Web crawlers are automated scripts that are used to browse the World Wide Web in a systematic manner. This work will be useful for parents and students to find institutions based on their set of preferences.

A. U. Angitha, M. Supriya

Using AUDIT Scores to Identify Synbiotic Supplement Effect in High-Risk Alcoholics

Chronic alcohol drinking results in increased intestinal permeability leading to translocation of gut-derived bacterial products. Elevated levels of these products in plasma can induce neuroinflammation probably linking to alcohol’s effects on brain function. Prior literature suggests that administration synbiotic may provide intestinal microbial balance and improve gut health. It may show the capacity to ameliorate brain functions in chronic alcohol drinkers. Twenty-one male patients with Alcohol Use Disorders Identification Test (AUDIT) score of 8 or above were administered with synbiotic preparation containing seven probiotics species and three prebiotics once a day before bedtime for eight weeks. There were significantly improved total AUDIT scores (p  = 0.001), and the data showed significant decreases in scores of the frequency of consuming and blackouts problems from alcohol drinking (p = 0.011 and 0.014, respectively). No other differences were observed between trials (p > 0.05). These findings suggested that synbiotic consumption could improve the alcohol consumption and addiction levels. The synbiotic may help to prevent and treat alcoholic illnesses. Further investigations for the synbiotic supplement effect on the gut–brain axis lessening the degree of alcohol-induced neuroinflammation in high-risk alcoholics should be studied.

Vachrintr Sirisapsombat, Chaiyavat Chaiyasut, Phuttharaksa Phumcharoen, Parama Pratummas, Sasithorn Sirilun, Thamthiwat Nararatwanchai, Phakkharawat Sittiprapaporn

Learning-Based Macronutrient Detection Through Plant Leaf

Singh, Amit Budihal, Suneeta V.The paper proposes a deep learning framework using two deep learning architectures, Keras and Pytorch to analyze the three macronutrients present in the plants basically nitrogen (N), phosphorous (P), potassium (K), i.e., NPK by the convolutional neural network (CNN). Agriculture is the backbone for the economy of a country, especially in the developing nations. Demand for food increases with an increase in population. To meet the increasing need for food, farmers need to maximize the productivity and balance the economy to reduce the losses. The plants require various minerals and nutrients for healthy growth and fruit development. Plant nutrients should be in proper proportion to keep plant healthier and less susceptible to pests. The nutrient analysis can be done by two techniques invasive and non-invasive techniques with their own advantages and disadvantages. Invasive or traditional methods are time-consuming and are costly, whereas non-invasive methods have proved its significance in recent years. The proposed methods are cost-effective and consume less time compared to conventional methods. The proposed framework provides an accuracy of 91% using Keras and 95% using Pytorch.

Amit Singh, Suneeta V. Budihal

Characteristics of Karawitan Musicians’ Brain: sLORETA Investigation

The fast development of music research prompts numerous interdisciplinary issues. In the neuroscience field of study, music is being examined related to its impact on the cognitive process or the psychological process behind it. These investigations inspire music’s integration to numerous subjects, for example, neuroscience and neuropsychology. A few previous studies showed the distinction between musicians and non-musicians regarding brain structure and brain activities. Instead of differentiating brain activity between musician and non-musician, the present study demonstrated the different brain activity while musicians listened to music regarding their musical experience. Applying the electroencephalography (EEG) recording and source localization in the exploratory methodology toward Karawitan musicians (N = 20), the outcomes demonstrated higher brain activities in tuning into recognizable music, Gendhing Lancaran, Javanese traditional music. In addition, the dominant brain activities happened in the temporal lobe while Karawitan musicians listened to Gendhing Lancaran, Javanese traditional music.

Indra K. Wardani, Djohan, Fortunata Tyasrinestu, Phakkharawat Sittiprapaporn

Automatic Detection of Parkinson Speech Under Noisy Environment

This work primarily aims to automatically detect patients who are suffering from Parkinson’s disease (PD) in comparison to the individuals who are healthy, through voice samples under clean and different noisy environmental conditions. The dataset was subjected to colored noises, electronic noises and natural noise. A feature vector comprising seven mean spectral features and two mean temporal features have been extracted. The performance of the PD detection model, configured by different classifiers of K- nearest neighbor (KNN), Extreme Gradient Boost, and Classification and Regression Trees (CART) have been analyzed under varying noisy environments. The proposed model for PD detection offers 97.01% accuracy for noise free dataset with KNN classifier and it also performs optimally even in the presence of varying noises. All colored noise samples gave superior classification accuracy with KNN classifier and all electronic and natural noises gave best accuracy with Extreme Gradient Boost classifier.

R. Janani Jayashree, Sneha Ganesh, Sanjana C. Karanth, S. Lalitha

Voice Conversion Using Spectral Mapping and TD-PSOLA

Kannan, Srinivasan Raju, Pooja. R. Madhav, R. Sai Surya Tripathi, ShikhaIn this paper, we propose a novel approach for a voice conversion system that makes effective use of spectral characteristics and excitation information, to optimally morph voice. This work addresses some key issues that are not adequately addressed in reported literature and achieves a more holistic voice conversion system. This is achieved using a strategic combination of line spectral frequencies (LSFs) to minimize the effects of over smoothing, a neural network for performing nonlinear spectral mapping and time-domain pitch synchronous overlap add to account for the interaction of excitation signal with the vocal tract. Within this proposed system, two different methods of pitch modification have been suggested, and the performance of these is compared with existing models of comparable complexity. The proposed methods have an average LSF performance index of 0.4082 and 0.4008, respectively, which is higher than existing similar work reported.

Srinivasan Kannan, Pooja. R. Raju, R. Sai Surya Madhav, Shikha Tripathi

Haze Removal Using Generative Adversarial Network

Sanjay, Amrita Nair, J. Jyothisha Gopakumar, G.The problem of haze removal has been addressed in many computer vision researches. Haze removal is the process of eliminating the degradation present in hazy images and getting the clearer counterpart. The presence of haze distorts the image, and as a result, it will be difficult to apply various image processing techniques on such images. The challenging aspect in haze removal arises due to the lack of depth information in images degraded by haze. The earlier methods for haze removal include various hand-designed priors, usage of the atmospheric scattering model or estimation of the transmission map of the image. The limitation with these models is that they are heavily dependent on the assumption of a good prior. In recent years, various models have been proposed which effectively remove the degradation caused by haze in images using various convolutional neural network architectures. This paper reviews a model which performs haze removal on a single image using generative adversarial network (GAN). The main advantage of this method is that it does not require the transmission map of the image to be explicitly calculated. The model was evaluated using NYU depth dataset and 0-Haze dataset. The model was able to significantly enhance the quality of the images by generating the corresponding haze-free counterpart. The model was evaluated using the peak signal-to-noise ratio and structural similarity index.

Amrita Sanjay, J. Jyothisha Nair, G. Gopakumar

Natural Language Processing


Fake News Detection Using Passive-Aggressive Classifier and Other Machine Learning Algorithms

Fake news means false facts generated for deceiving the readers. The generation of fake news has become very easy which can mislead people and cause panic. Therefore, fake news detection is gaining prominence in research field. As a solution, this paper aims at finding the best possible algorithms to detect fake news. In this paper, term frequency–inverse document frequency (TFIDF) as well as count vector techniques is used separately for text preprocessing. Six machine learning algorithms namely passive-aggressive classifier (PAC), naive Bayes (NB), random forest (RF), logistic regression (LR), support vector machine (SVM), and stochastic gradient descent (SGD) are compared using evaluation metrics such as accuracy, precision, recall, and F1 score, The results have shown that the TFIDF is a better text preprocessing technique. PAC and SVM algorithms show the best performance for the considered dataset.

K. Nagashri, J. Sangeetha

Generative Adversarial Network-Based Language Identification for Closely Related Same Language Family

Kar, Ashish Sunitha Hiremath, P. G. Gangisetty, ShankarThe discrimination between similar languages is one of the main challenges in automatic language identification. In this paper, we address this problem by proposing a generative adversarial network-based language identification method for identifying the sentences from closely related languages of same language family. The proposed method works on dual-reward feedback learning comprising of generator to generate nearly close language sentences, discriminator for determining how similar the generated sentences are to that of the training data and classifier for optimal prediction of the correct label. We evaluate the proposed model for pairs of languages and overall testing data comparison on Indo-Aryan languages dataset [12]. The effectiveness of our method is demonstrated in comparison to other existing state-of-the-art methods.

Ashish Kar, P. G. Sunitha Hiremath, Shankar Gangisetty

Statistical and Neural Machine Translation for Manipuri-English on Intelligence Domain

Rahul, Laishram Meetei, Loitongbam Sanayai Jayanna, H. S.This paper describes the development and results of Manipuri-English machine translation system built on an intelligence domain. Manipuri is an under-resourced Tibeto-Burman language that is spoken mainly in the North-Eastern states of India. A total of 56,678 Manipuri-English parallel corpora from intelligence domain based on the open-source intelligence (OSINT) keywords and phrases are collected for the experiment. An evaluation of statistical machine translation (SMT) and neural machine translation (NMT) is carried out in terms of BLEU score. A BLEU score of 23.91 is achieved with the SMT-based approach which is outperformed by the NMT-based system with a BLEU score of 40.67. Further, a language-specific morphological analysis based on the suffixes is investigated. The findings on the incorporation of morphological analysis report a BLEU score of 25.03 with the SMT and a BLEU score of 44 with NMT, both of which are a significant improvement.

Laishram Rahul, Loitongbam Sanayai Meetei, H. S. Jayanna

Fake Review Detection Using Hybrid Ensemble Learning

Hegde, Sindhu Raj Rai, Raghu Sunitha Hiremath, P. G. Gangisetty, ShankarOpinion spam on online restaurant review sites are a major problem as the reviews influence the users’ choice to visit or not to a restaurant. In this paper, we address the problem of detecting genuine and fake reviews in restaurant online reviews. We propose a fake review detection technique comprising data preprocessing, detection and ensemble learning that learns the reviews and their features to filter out the fake reviews. Initially, we preprocess to obtain the refined reviews and employ two independent classifiers using deep machine learning and feature-based machine learning techniques for detection. These classifiers tackle the problem in two aspects, i.e., the deep machine learning model learns the word distributions and the feature-based machine learning model extracts the relevant features from the reviews. Finally, a hybrid ensemble model from the two classifiers are built to detect the genuine and fake reviews. The experimental analysis of the proposed approach on Yelp datasets outperforms the existing state-of-the-art methods.

Sindhu Hegde, Raghu Raj Rai, P. G. Sunitha Hiremath, Shankar Gangisetty

Utilizing Corpus Statistics for Assamese Word Sense Disambiguation

Classification or categorization of a word based on its meaning in respect to a context is one of the major problems in Natural Language Processing (NLP). Such a problem is termed as Word Sense Disambiguation (WSD), and the mentioned problem is seen to be prevalent in all languages across the globe. However, in Indian languages, WSD poses greater challenges due to limitation of digital resources and lack of UNICODE. In this paper, we have made an attempt to highlight the efforts put by researchers to overcome WSD. It is also to be mentioned that for the said purpose, two WSD algorithms for Assamese language WSD are contrasted while asserting the corpus statistics in the approach. Of the two aforementioned WSD algorithms, the first is applied using the Lesk algorithm simpler, while the second is exercised to determine the probability of words and phrases on grounds of condition that co-occur with every meaning of an ambiguous word in disambiguation. Both the algorithms delivered affirmative results for a trained set of corpus. However, compared to the second, the Lesk algorithm yielded better results in terms of overall efficiency of the system developed in comparison to words and phrases co-occurrence.

Nomi Baruah, Arjun Gogoi, Shikhar Kr. Sarma, Randeep Borah

A Novel Approach to Text Summarisation Using Topic Modelling and Noun Phrase Extraction

Lal, Nikhil M. Krishnanunni, S. Vijayakumar, Vishnu Vaishnavi, N. Siji Rani , S. Deepa Raj, K.Over the past few years, one of the remarkable developments that happened on the web is the rapid growth of textual data. This substantial increase, however, induces a complication in the retrieval of vital information from the digitized collection of data. The conventional technique used to tackle this problem is Automatic Text Summarisation. This technique extracts the essential words or sentences from the data and summarises it without affecting the semantics. Automatic text summarisation is classified into two, Extractive and Abstractive. The Extractive method summarises a document by selecting the important words or sentences from it, based on some attributes while the Abstractive method attempts to generate its summary from the semantics of the data. In this paper, we propose a novel approach in Extractive text summarisation by using a new sentence scoring parameter. The experimental results show that the proposed sentence scoring parameter improves the performance of the Extractive text summariser, when compared with other summarisation models. To validate our proposed model, we compared it with four commonly used summarisation models on grounds of ROUGE-1 score and F1 score.

Nikhil M. Lal, S. Krishnanunni, Vishnu Vijayakumar, N. Vaishnavi, S. Siji Rani , K. Deepa Raj

Part of Speech Tagging Using Bi-LSTM-CRF and Performance Evaluation Based on Tagging Accuracy

Part of speech tagging (POS) refers to the computational task of identifying related parts of speech for specific words in text documents. A research challenge is to use various techniques to identify and utilize these tags to improve several natural language processing applications. In this paper, a bidirectional long short-term with conditional random field (denoted as Bi-LSTM-CRF) model has been proposed for POS tagging. This novel model trained on the named entity resolution dataset is compared with other recurrent neural network models such as bidirectional long short-term model networks and long short-term model with conditional random field. Bi-LSTM-CRF model is applied to the annotated NER dataset and its tagging accuracy and F1-score are compared with the other pre-existing models. Experimental results show that Bi-LSTM-CRF provides better results for POS tagging. The Bi-LSTM-CRF model is competitive on the annotated NER dataset for English to produce greater accuracy and F1-score and outperform the rest of the models.

Shilpa Kamath, Chaitra Shivanagoudar, K. G. Karibasappa

Clustering Research Papers: A Qualitative Study of Concatenated Power Means Sentence Embeddings over Centroid Sentence Embeddings

Gaikwad, Devashish Yelnoorkar, Venkatesh Jadhav, Atharva Haribhakta, YashodharaMathematical average of word embeddings is a common baseline for sentence embedding techniques which typically fall short of the performance of more complex models such as BERT and InferSent. There has been significant improvement in the field of sentence embeddings and especially towards the development of universal sentence encoder that can be used for transfer learning in a wide variety of downstream tasks. Academic paper retrieval systems are widely used in academic institutions to store and categorise scientific papers and find connections between them using citation links, but these methods do not account for the content of the papers. For unsupervised clustering of these papers, a new approach of sentence embeddings is proposed using concatenated power means sentence embeddings and centroid sentence embeddings. The sentence embeddings so created are clustered using K-means clustering algorithm. The results show a clear increase of 47.94% in cosine distance of nearest papers using concatenated power means sentence embeddings with respect to baseline centroid embeddings for the highest performing GloVe models proving that the computationally inexpensive P-Means clustering sentence embeddings can be used for unsupervised clustering of scientific research papers using their abstracts.

Devashish Gaikwad, Venkatesh Yelnoorkar, Atharva Jadhav, Yashodhara Haribhakta

Semantic Sensitive TF-IDF to Determine Word Relevance in Documents

Jalilifard, Amir CaridVinicius Fernandes Mansano, Alex Fernandes Cristo, Rogers S. Da Fonseca, Felipe Penhorate CarvalhoKeyword extraction has received an increasing attention as an important research topic which can lead to have advancements in diverse applications such as document context categorization, text indexing and document classification. In this paper we propose STF-IDF, a novel semantic method based on TF-IDF, for scoring word importance of informal documents in a corpus. A set of nearly four million documents from health-care social media was collected and was trained in order to draw semantic model and to find the word embeddings. Then, the features of semantic space were utilized to rearrange the original TF-IDF scores through an iterative solution so as to improve the moderate performance of this algorithm on informal texts. After testing the proposed method with 160 randomly chosen documents, our method managed to decrease the TF-IDF mean error rate by a factor of 50% and reaching the mean error of 13.7%, as opposed to 27.2% of the original TF-IDF.

Amir Jalilifard, Vinicius Fernandes Caridá, Alex Fernandes Mansano, Rogers S. Cristo, Felipe Penhorate Carvalho da Fonseca

Web-Based Interactive Neuro-Psychometric Profiling to Identify Human Brain Communication and Miscommunication Processing

This study investigated the effect of individual brain communication processes on interpersonal communication and potential miscommunication by using a web-based interactive neuro-psychometric profiling tool, named Colored Brain Communication Inventory (CBCI). The brain communication process is influential patterns of individual communication and cultivates the potential of individuals to career or work in the future. The methodology used in this study was quantitative, surveys, and observation studies. The aim of this study was then to explore the brain clarity processing and distinguishing the miscommunication assessed by web-based interactive neuro-psychometric profiling instruments. All respondents involved in this study were equally divided between gender; males (50%) and females (50%). The impact of this study has practical implications for the respondents’ communication behavior toward individual developments. The impact on science was to know the correlation between the brain communication process on gender, creativity, and communication behavior by means of potential miscommunication process. The output of this study was a type of brain communication process that shows the tendency of nature, attitudes, and individual potential.

Arthur F. Carmazzi, Phakkharawat Sittiprapaporn

Seventh International Symposium on Computer Vision and the Internet (VisionNet’20)


Deep Visual Attention Based Transfer Clustering

Gunari, Akshaykumar Kudari, Shashidhar Veerappa Nadagadalli, Sukanya Goudnaik, Keerthi Tabib, Ramesh Ashok Mudenagudi, Uma Jamadandi, AdarshIn this paper, we propose a methodology to improvise the technique of Deep Transfer Clustering (DTC) when applied to the less variant data distribution. Clustering can be considered as the most important unsupervised learning problem. A simple definition of clustering can be stated as “the process of organizing objects into groups, whose members are similar in some way”. Image clustering is a crucial but challenging task in the domain machine learning and computer vision. We have discussed the clustering of the data collection where the data is less variant. We have discussed the improvement by using attention-based classifiers rather than regular classifiers as the initial feature extractors in the Deep Transfer Clustering. We have enforced the model to learn only the required region of interest in the images to get the differentiable and robust features that do not take into account the background. This paper is the improvement of the existing Deep Transfer clustering for less variant data distribution.

Akshaykumar Gunari, Shashidhar Veerappa Kudari, Sukanya Nadagadalli, Keerthi Goudnaik, Ramesh Ashok Tabib, Uma Mudenagudi, Adarsh Jamadandi

Video Retrieval Using Residual Networks

With the growing size of data across various different forms in today’s world, a lot of meaningful information needs to be extracted from huge amount of data. Specially the multimedia content on web is increasing rapidly; thus, the demand for searching and retrieval of the required multimedia data is also increasing. Hence, there is a need for a faster retrieval of required data for different queries such as image, video, audio and text. In this paper, we propose a video retrieval framework using residual networks (ResNet-34) based on the query image or video clip and retrieve the relevant or similar videos from the video dataset. The ResNet-34 with locality sensitive hashing algorithm provides a faster retrieval of the relevant or similar videos from the dataset. The retrieval efficiency is improved from quadratic to logarithmic efficiency class. We demonstrate the proposed method for nine different categories of you tube videos and obtain an overall precision rate of 84% which is comparable with the state of the art.

U. Tejaswi Nayak, C. Sujatha, Tanmayi V. Kamat, Padmashree Desai

Dynamic Search Paths for Visual Object Tracking

Gunisetty, Srivatsav Bommerla, Vamshi Krishna Dasari, Mokshanvitha Chava, Vennela Gopakumar, G.The long-term sub-track of visual object tracking challenge comprises of some of the most challenging scenarios like occlusion and target disappearance and reappearance. To this end, many deep learning solutions with multiple levels of detection have been proposed. Most of these solutions tend to re-identify a wrong target during the occlusion or disappearance as they start looking for the target in the entire frame. Instead, through this work, we intend to prove that predicting a probable search region for the target by understanding its trajectory and searching for a target in it will help in reducing the misidentifications and also aid in the increase of IoU. For this, we have utilized the trajectory modeling capabilities of the Kalman filter. With this proof of concept work, we achieved an average improvement of 37.37% in IoU in the sequences where we overperformed MBMD.

Srivatsav Gunisetty, Vamshi Krishna Bommerla, Mokshanvitha Dasari, Vennela Chava, G. Gopakumar

Thermal Facial Expression Recognition Using Modified ResNet152

Prabhakaran, Aiswarya K. Nair, Jyothisha J. Sarath, S.Facial expression for emotion detection has taken wide popularity with visible images using machine learning techniques and convolutional neural networks. However, emotion recognition from visible images is not much plausible as they are sensitive to light conditions and people can easily fake expression. In this paper, we propose a method for facial expression recognition with thermal images using ResNet152. Residual networks are easier to optimize, and can gain accuracy from considerably increased depth. The objective of this paper is to use a pre-trained modified ResNet152 to train thermal facial images in order to predict different emotions. We use natural visible and infrared facial expression (NVIE) dataset for emotion classification.

Aiswarya K. Prabhakaran, Jyothisha J. Nair, S. Sarath

Real-Time Retail Smart Space Optimization and Personalized Store Assortment with Two-Stage Object Detection Using Faster Regional Convolutional Neural Network

In the present-day scenario of the retail environment, there is a tendency of customers to do prolonged shopping. During their constant efforts to purchase products of their choice, they bat around the entire store. They choose a product and continue exploring for more. While their further exploration, there is a possibility that they might encounter a better product that may satisfy their needs. So, there is a tendency that they may pick up the new product, compare with the existing product and leave the latter behind if they find the new product to be of a better purpose to their use, causing the initial product to be misplaced. There might be a possibility that empty spaces be created between products that might look sparse and lower stock display if not properly monitored. The primary goal of this research is smart space management and personalized store assortment by using computer vision. That is, wherever there are empty spaces created, we constantly monitor, and whenever there is any product misplaced, we send an automated notification to corresponding staff. We use state-of-the-art computer vision technology to address this issue. All the processing is done in real time and the system is found to be functionally very stable and works under all ideal conditions.

Nitin Vamsi Dantu, Shriram K. Vasudevan

2D-Image Super-Resolution on Heritage Site

One proposed method for image enhancement is single image super-resolution. For this task, many convolutional neural networks-based models were designed. These convolutional neural networks-based models perform better than the other approaches in quality measurements like structural similarity and peak signal-to-noise ratio (PSNR). Resulting super-resolved image quality is dependent on choice of a loss function. Ongoing work is to a great extent dependent on advancing mean squared reconstruction error. But PSNR and structural similarity values cannot give fine details in an image and provide higher values with unsatisfying quality. Hence, generative adversarial networks model was introduced for this problem in recent years. In this paper, image super-resolution (SR) is done with a generative adversarial network (GAN). It is the first method used for 4 × upscaling factors. Proposed approach calculates loss function which is combination of two loss functions like content and adversarial loss.

Sheetal Pyatigoudar, S. M. Meena, Sunil V. Gurlahosur, Uday Kulkarni

Automated Detection of Liver Tumor Using Deep Learning

Abhijith, V. Biju, Mable Gopakumar, Sachin Gomez, Sharon Andrea Mathew, TessyCancer has been recognized by the World Health Organization as the second leading reason for deaths around the world. With the rise in population, Hepatocellular Carcinoma (HCC) cases have increased due to a lack of early diagnosis and treatment. Conventionally, CT or MRI scans of affected livers undergo manual examination by trained professionals, which usually takes substantial time and effort. With the rising number of cases, this process needs to be sped up. Using deep learning models for medical image segmentation has proven to be an effective method. The proposed approach of deep learning model uses a 2D U-net architecture constructed on fully convolutional network (FCN). The U-net architecture consists of three layers; the contracting/down-sampling, the expanding/up-sampling, and the bottleneck layer which acts as a median between the other two layers. The dataset consists of computed tomography images for training and testing respectively where each scan is in a 3D image format called NIfTI (.nii) and is of variable sizes. Our proposed model is enveloped in application software, where the front end provides a minimalist and intuitive user experience. Using this approach, we received an accuracy of 0.71 using the dice similarity metric. The main benefit of having an application software approach is the ease of adoption in places where such a solution is required to save valuable time and effort.

V. Abhijith, Mable Biju, Sachin Gopakumar, Sharon Andrea Gomez, Tessy Mathew

Breast Mass Classification Using Classic Neural Network Architecture and Support Vector Machine

Priya, R. Sreelekshmi, V. Nair, Jyothisha J. Gopakumar, G.According to WHO, the most dangerous disease prevailing among women is breast cancer. It is among one of the diseases that is untraceable in the beginning. About 1 in 8 women suffer breast cancer and even results in the removal of their breast. In this domain, a novel experiment to classify breast cancer using convolutional neural network and fuzzy system is introduced. A combination of convolution neural network and fuzzy system has been devised for grouping similar masses of benign and malignant in mammography database based on the mass area in breast. The mammography images are taken for image enhancement and image segmentation for identifying the mass area and the classic neural network architecture (Alexnet) performs the feature extraction. After that it is followed by fuzzy system for finding how much denser the malignant or benign cancer is. A well-known classic neural network architecture AlexNet is employed and is fine tuned to group similar classes. The fully connected (fc) layer is replaced with support vector machine (SVM) to improve the classification effectiveness. The results are derived using the following publicly available datasets: (1) digital database for screening mammography (DDSM), (2) curated breast imaging subset of DDSM (CBIS-DDSM) and (3) mammography image analysis society (MIAS). Data augmentation is also performed to increase the training samples and to achieve better accuracy.

R. Priya, V. Sreelekshmi, Jyothisha J. Nair, G. Gopakumar

Symposium on Emerging Topics in Computing and Communications (SETCAC’20)


Providing Software Asset Management Compliance in Green Deployment Algorithm

Baillon-Bachoc, Noe Caron, Eddy Chevalier, Arthur Vion, Anne-LucieToday, the use of software is generally regulated by licenses, whether they are free or paid and with or without access to their sources. The world of licenses is very vast and unknown. Often only the public version is known (a software purchase corresponds to a license). For enterprises, the reality is much more complex, especially for main software publishers. Very few, if any, deployment algorithm takes software asset management (SAM) considerations into account when placing software on Cloud architecture. This could have huge financial impact on the company using theses software. In this article, we present the SAM problem more deeply; then, after expressing our problem mathematically, we present GreenSAM, our multi-parametric heuristic handling performance and energy parameters as well as SAM considerations. We will then show the use of this heuristic on two realistic situations, first with an Oracle Database deployment and second with a larger scenario of managing a small OpenStack platform deployment. In both cases, we will compare GreenSAM with other heuristics to show how it handles the performance/energy criteria and the SAM compliance.

Noëlle Baillon-Bachoc, Eddy Caron, Arthur Chevalier, Anne-Lucie Vion

An Analysis of Rainstreak Modeling as a Noise Parameter Using Deep Learning Techniques

Akaash, B. Aarthi, R.Outdoor vision systems (OVS) play a vital role in the surveillance of the environment. However, the images and videos captured by these systems could be severely tampered by the sharp intensity changes brought about by adverse weather and climatic conditions. In this work, synthetically prepared rain images are modeled to visualize the randomly distributed rainstreak patterns as noise. The analysis has been performed using various deep learning networks such as auto-encoders with and without skip connections and denoising convolutional neural networks (DnCNN). The best model for this process has been suggested based on mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) obtained by comparing the original and the reconstructed image.

B. Akaash, R. Aarthi

Diverting Tantrum Behavior Using Percussion Instrument on Autistic Spectrum Disorders

The aim of this study was to distract the tantrum of the patient with autistic spectrum disorders (ASD). Children with tantrums disturbed others and even the children themselves, as well as the activities they were doing. From this condition, we attempted to divert the tantrums of the children with ASD by using percussion instruments. The use of musical instruments is an effective method to redirect the behavior intended to be changed since musical instruments are interesting and pleasing objects for children suffering from ASD. Owing to the matter, music and its instruments have a chance to be utilized in diverting the behavior of ASD children. This experiment by single subject design ABA took a single subject of ASD which was 7 years old. The musical instruments applied are five percussions which are tambourine, glockenspiel, tifa, maracas, and bell. The result showed that the subject throwing tantrums are hardly managed in the first baseline. It was supported by the mean baseline, which is 7.3 min, whereas the average result in treatment showed 2.3 min. It revealed that the time span of the subject’s response in treatment was less than in the baseline. However, in the treatment phase, the subject’s behavior was distracted. It occurred because the subject seemed interested and enthusiastic as playing the percussions. The treatment also increased the cognitive aspect of the subject, focusing on ongoing activities. The subject could play bell based on the therapist’s instruction and holding on to it until 15 min. The percussion instrument was able to improve the subject’s focus on playing bell and distract the tantrum.

Zefanya Lintang, Djohan, Fortunata Tyasrinestu, Phakkharawat Sittiprapaporn

Text Sentiment Analysis Using Artificial Intelligence Techniques

In today’s world, data is being generated with such high velocity and variety that analyzing such large volumes of data to extract meaningful results is a taxing job and manually impossible. Developing methods to analyze such large volumes of data for the purpose of finding hidden patterns to achieve meaningful interpretations is necessary for many organizations for making informed decisions. Sentiment analysis is one such method that is used to interpret the emotions represented by text data. There exists a broad range of applications for sentiment analysis. Public opinion is an important “business insight”. All businesses are interested in analyzing the consumer behavior, understanding their needs, understanding their likes and dislikes and their buying patterns, and as more and more people are becoming vocal about their preferences, the data required by these companies is becoming readily available on blogs and social media platforms, but the analysis of such large amount of raw data to derive useful conclusions is a hectic task if tried to perform manually. In such cases, sentiment analysis can be used to analyze this raw data. Sentiment analysis can be used for a variety of needs ranging from understanding the public opinion of a government policy to assigning movie ratings from analysis of viewer ratings. This method is especially an important factor in social media monitoring to gain a wider public opinion about a topic. NLP tools available today can be used to efficiently analyze this raw text and classify texts as positive, negative, or neutral. Different machine learning algorithms like random forest, logistic regression, and support vector machine can be used to train the model using the features extracted by the NLP techniques. The trained model can then be used to predict the polarity of the raw input data. ROC and PR curves have been plotted to check the accuracy of the algorithms.

Sanskriti Srivastava, M. Vergin Raja Sarobin, Jani Anbarasi, Namrata Sankaran

Internet Performance Profiling of Countries

The paper presents research into data capture and analysis techniques for creating spatial and temporal Internet quality of service (QoS) performance profiles of countries. Active Internet probing and traffic monitoring techniques are used, facilitated through the European RIPE Atlas project to capture raw QoS measurements. The research goal is to contribute toward developing large-scale network QoS performance profiling and testing methods for local to long-range and global-range Internet QoS performance analysis. The range of stakeholder interest in such profiles is wide, from network owners and Internet service providers (ISPs) to Internet service provisioning consultants, and corporate, business, and individual users. Also, applications are wide such as detection and location of temporal traffic bottleneck and faults, bottleneck incidence behavior as a function of geographic and temporal service demands, to Internet service level agreements (SLAs) and their policing. Twenty-six European countries are examined and profiled on the basis of a bidirectional north–south and east–west ‘compass profiling’ methodology over a one-month period. The worst-case scenarios detected are presented as an example. The results may serve as an initial benchmark for mapping evolving performance profiles over longer or continuous periods of time, employing more geographical spread testing probes, and a mix of profiling methodologies especially for verification purposes. A similar approach may be taken to regional, international, and intercontinental Internet QoS profiling.

Nikolay Todorov, Ivan Ganchev, Máirtín O’Droma

Modelling a Folded N-Hypercube Topology for Migration in Fog Computing

Roig, Pedro Juan Alcaraz, Salvador Gilly, Katja Juiz, CarlosIoT moving devices need to have their associated VMs as close as possible so as to minimize latency, jitter, bandwidth use and even power consumption. It implies that Data Centers in Fog Computing environments must be ready to move VMs around its different hosts on a discretionary manner in order to cope with such movements. In this paper, a Folded N-Hypercube switching infrastructure is being modelled according to different views, such as arithmetic, logical and algebraic, paying special attention as to how to manage VM migrations around the Fog domain.

Pedro Juan Roig, Salvador Alcaraz, Katja Gilly, Carlos Juiz

Brain Electric Microstate of Karawitan Musicians’ Brain in Traditional Music Perception

The rapid development of music research leads to many interdisciplinary topics. In the neuroscience field of study, music is being studied related to either its effect on the cognitive process or the cognitive process behind it. Generally, neuroscience is a field of study to focus on the music’s integration. The difference in brain structure and activities between non-musician and musicians has been shown by several previous studies. Instead of differentiating brain activity between musician and non-musician, the present study demonstrated the different brain activity while musicians listened to music regarding their musical experience. Applying the electroencephalography recording in the experimental approach toward Karawitan musicians, the results showed higher brain activity in listening to familiar music, Gendhing Lancaran, traditional music of Java. In addition, the dominant brain activity happened in the temporal lobe while Karawitan musicians listened to Gendhing Lancaran, the traditional music of Java.

Indra K. Wardani, Djohan, Fortunata Tyasrinestu, Phakkharawat Sittiprapaporn

MIMO-Based 5G Data Communication Systems

Many of the main targets or expectations that need to be met in the immediate term, i.e., after 5G, are expanded efficiency, higher data rate, reduced average latency, and enhanced coverage quality. Energy use for the networks is a big concern. To satisfy these requirements, there need to be dramatic changes in the design of the telecommunications network. This paper discusses the findings of a comprehensive study on the telecommunications network of the fifth generation (5G), including some of the main new innovations that further develop the infrastructure. The main subject of this comprehensive study is the 5G wireless network, massive MIMO infrastructure, and device-to-device compatibility (D2D). A comprehensive survey is provided in Sect. 4 addressing detailed study about deployments and predictions.

Shubham Mathesul, Ayush Rambhad, Parth Shrivastav, Sudhanshu Gonge

Android Malware Classification Based on Static Features of an Application

Android is the most sought-after mobile platform that has changed what mobiles can do. Due to this, a continuous increase in android malware applications has been seen that poses a significant hazard to users. Thus, the detection of malware applications in the Android environment has become a trending research field for cybersecurity researchers. Android malware detection depends on characterizing the Android application’s functionalities. Over the years, malware has evolved and has become more sophisticated. Hence, it cannot be detected only using a single static feature as it might result in a high number of false negatives. We propose a detection model in this paper that accurately classifies the samples as malware or benign with fewer false positives and false negatives. We have used string features that include suspicious API calls, used permissions, requested permissions, filtered intents, hardware components, and restricted API calls. We have then employed four machine learning algorithms, namely, Ridge Classifier, XGBoost Classifier, Random Forest, and Support Vector Classifier to evaluate the effectiveness of the binary feature vector formed by the combination of these string features. It was noted that Random Forest achieved the highest score for accuracy, precision, recall, area under curve, and F1 score.

S. D. Ashwini, Manisha Pai, J. Sangeetha

Performance Evaluation of Cross-Layer Routing Metrics for Multi-radio Wireless Mesh Network

Narayan, D. G. Naravani, Mouna Shinde, SumedhaWireless Mesh Networks (WMNs) are emerging as future-generation technologies for back-haul connectivity of different types of networks. They are multi-hop networks consisting of mesh nodes, mesh clients and mesh routers. To achieve high performance, these networks use Multi-Channel Multiple Radio (MCMR) capabilities of mesh routers. However, QoS degrades due to the introduction of inter-flow interference and intra-flow interference by these nodes. Thus, there is a need to design a routing protocol that considers interference in the network. Furthermore, as the traditional approaches of protocol design lacks in improving QoS, the cross-layer metrics are designed by using the information from physical, MAC and network layer. In this paper, we investigated and analysed the performance of multi-radio cross-layer routing metrics. We performed extensive quantitative analysis in NS-2 using OLSR protocol. The performance differentials and trade-off analysis is carried out using five QoS parameters.

D. G. Narayan, Mouna Naravani, Sumedha Shinde

Modelling a Plain N-Hypercube Topology for Migration in Fog Computing

Roig, Pedro Juan Alcaraz, Salvador Gilly, Katja Juiz, CarlosFog Computing deployments need consolidated Data Center infrastructures in order to get optimal performances in those special environments. One of the key points in attaining such achievements may be the implementation of Data Center topologies with enhanced features for a relatively small number of users, although ready for dealing with occasional traffic peaks. In this paper, a plain N-Hypercube switching infrastructure is modelled in different ways, such as using arithmetic, logical and algebraic ways, focusing on its capabilities to manage VM migrations among hosts within such a topology.

Pedro Juan Roig, Salvador Alcaraz, Katja Gilly, Carlos Juiz


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