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In this paper, a novel method is proposed to reduce the number of route misdirection to increase network throughput. This method increases the network throughput by accurately sending the packets from source node to destination node without any packet loss, which ensures Quality of Services (QoS) routing services. These metrics are used for making better routing decisions to avoid the packets being misrouted in the network. The proposed method operates under two phases: flow control and QoS routing phase. Wireless sensor network dynamically changes its topological structure since the path between source and destination nodes varies at the rapid instance of time. Hence, the flow control phase is used to adjust the packet flow through a proper link to reach its destination node. The flow control phase effectively designs an improved Random Waypoint Mobility model that utilizes three metrics for avoiding the packets being misdirected. In the second phase, the packets are routed through proper routes after avoiding the misdirected routes using an adaptive QoS routing protocol. This helps to increase the throughput of the network and this utilizes the three metrics for accurate identification of routes. The performance of the proposed method is evaluated the various metrics like network throughput (98.595 kbps), network lifetime (346 s) and delay (0.922 s). The proposed method is compared with existing QoS routing algorithms. The results show that the proposed method has improved its throughput level by reducing the total number of packets being misrouted in the network.

The current study aimed to isolate bacteria that harbour various animal food products like meat, chicken and seafoods collected from the abattoir, butcher shops and local seafood market and to determine the antimicrobial resistance pattern of isolated pathogens which are responsible for various foodborne illnesses in human beings. A total of forty raw animal product samples were collected from the abattoir, butcher shops and local seafood market of Visakhapatnam. The samples selected for the study include raw chicken, meat, crab, prawns and different varieties of fish. A classic random sampling technique was employed to collect the study samples. All the samples were processed immediately using standard microbiological protocols. The bacteria isolation and characterization were done by studying morphological characteristics with staining methods, cultural characteristics by isolating and growing the pathogenic microorganisms in various selective and differential culture media. Antimicrobial susceptibility testing was performed by the Kirby -Bauer method by following Clinical and Laboratory Standards Institute (CLSI) guidelines. EDTA-Disc Potentiation Test and Imipenem-EDTA Double disc synergy test are used to detect the metallo beta-lactamase production of isolated pathogens. The highest number of isolates belong to Salmonella species (18), Pseudomonas aeruginosa (18) followed by Vibrio species (14) and few isolates belong to Enterobacter species (4). Majority of the microbial isolates obtained in the current study were multidrug resistant. The isolates from the abattoir environments, slaughterhouses, fish markets were found to exhibit variable resistance pattern to aminoglycosides, macrolides, β-lactams, cephalosporins, quinolone antibiotics used in the present study and at the same time most of them were sensitive to carbapenem antibiotic imipenem. Antimicrobial resistance (AMR) prevents the designing and assessment of effective interventions. If such a link can be established, then the tracking of antibiotic use and consumption data could be furthermore used as a surrogate indicator for the risk of potential antibiotic resistance (ABR) emergence.

In recent times, congestion in traffic is bit cost effective and several traffic management methods are utilized to balance the traffic as per the capacity of roads. However, many methods fails in pre-analyzing the network events to regulate the traffic flow. Also, the dynamic movement of vehicles fails in adapting the vehicle units to follow the changing topology. Hence, it is necessary for packet forwarding between source and destination nodes in a precise manner, where the packet loss and communication overhead should be in control. In this paper, we utilize the concept of Connected Dominating Set (CDS) approach in traffic management for reducing the flow of traffic streaming in main routes. The CDS approach dynamically adapts itself with the changing topology and acts as a virtual backbone to reduce traffic overhead. It restricts the increased flow of vehicles to the dominators, since traffic congestion in such areas increases. The CDS forwarding (CDSF) finds the dominating or high congestion routes and diverts the traffic to other areas based on the level of traffic in sub-routes. The CDSF method works on an assumption that edges on road segments has multiple vehicles, where it increases the network efficiency. However, we make careful consideration of choosing the other road segments to traverse the required packets from source to destination with reduced packet loss. The construction of a virtual backbone network with series of neighboring backbone nodes traverses efficiently the packets. Further, choosing vehicles with low velocity improves the probability of connectivity in a specific area and increases the network throughput. This improves the re-routing flexibility and makes the packets to reach the destination without traffic congestion in VANETs. The CDSF method and control strategies are tested in NS2 and SUMO simulator under different traffic conditions. The results of simulation show that CDSF traffic management system reduces significantly the traffic congestion and overall traffic cost that includes CDF, average latency, average end-to-end delay, Hop count, packet error rate, percentage delivery ratio, data delivery rate, throughput. The result shows that the proposed method 0.345 times improved packet delivery ratio, 0.35 times improved throughput and 0.7 times reduced delay than the existing methods. Further, the CDSF method controls the traffic density by reducing the maximum queue length and traffic controlling time.

Ocean mines are the major threat to the safety of great vessels and other living beings in the marine life. It is a self-contained explosive device placed in water to destroy ships or submarines. Due to various factors like variations in operating and target shapes, environmental conditions, presence of spatially varying clutter, compositions and orientation, detection and classification of sonar imagery with respect to underwater objects is a complicated problem. It is well known that many post processing techniques in image processing have done to receive high resolution images to distinguish the objects. However the mentioned technique needs a special method to detect the metal from the usual sub bottom materials mainly rocks. Hence the data collection made in simulated environment locating metals in rock bed and collected with the sonar and the distinguished features of metals from rock have been identified with the totally different approach called intruder detection technique using data mining/machine learning. This paper proposes a novel approach for discriminating and detection of objects in underwater environment with accuracy of 90% (full feature set) and 86% (selected feature set). Hence, it is quite revealing that the new technique is better in classification of mine like objects in underwater, justified with samples of sonar data sets.

India has the second largest number of people with type 2 diabetes (T2D) globally. Epidemiological evidence indicates that consumption of white rice is positively associated with T2D risk, while intake of brown rice is inversely associated. Thus, we explored the effect of substituting brown rice for white rice on T2D risk factors among adults in urban South India. A total of 166 overweight (BMI ≥ 23 kg/m2) adults aged 25–65 years were enrolled in a randomised cross-over trial in Chennai, India. Interventions were a parboiled brown rice or white rice regimen providing two ad libitum meals/d, 6 d/week for 3 months with a 2-week washout period. Primary outcomes were blood glucose, insulin, glycosylated Hb (HbA1c), insulin resistance (homeostasis model assessment of insulin resistance) and lipids. High-sensitivity C-reactive protein (hs-CRP) was a secondary outcome. We did not observe significant between-group differences for primary outcomes among all participants. However, a significant reduction in HbA1c was observed in the brown rice group among participants with the metabolic syndrome (−0·18 (se 0·08) %) relative to those without the metabolic syndrome (0·05 (se 0·05) %) (P-for-heterogeneity = 0·02). Improvements in HbA1c, total and LDL-cholesterol were observed in the brown rice group among participants with a BMI ≥ 25 kg/m2 compared with those with a BMI < 25 kg/m2 (P-for-heterogeneity < 0·05). We observed a smaller increase in hs-CRP in the brown (0·03 (sd 2·12) mg/l) compared with white rice group (0·63 (sd 2·35) mg/l) (P = 0·04). In conclusion, substituting brown rice for white rice showed a potential benefit on HbA1c among participants with the metabolic syndrome and an elevated BMI. A small benefit on inflammation was also observed.

The exact interpretation of structure of the Earth is essential to avoid drilling of dry holes or locating hydrocarbon reservoirs in faulty regions. The economic approaches are required to locate the reservoirs without damaging the tectonic plates of the Earth. The identification of gas chimneys provides an impressive approach to indirectly interpret the hydrocarbon reservoirs as the chimneys form the migration pathway for gas from reservoirs, and it looks like a gas cloud in the seismic data. The proposed method using biogeography-based optimization (BBO) with supervised neural networks enables the effective interpretation of chimneys and hydrocarbons. Seismic attributes are measured on the pre-processed seismic data, where the attributes play an important role in providing qualitative information on structure of the Earth. Continuity, instantaneous and amplitude attributes are measured to provide the details on the areas of discontinuity, bright-spot regions with high amplitude and low frequency. The calculated attributes are picked from selected locations in the pre-processed seismic data, and the hybrid combination of attributes enables effective, economic chimney identification and provides indications to locate the reservoirs. The redundant and highly correlated features are eliminated using BBO which in turn improves the classification accuracy. BBO algorithm retains the best solution in each generation, and the fitness of the algorithm is verified with supervised neural networks. The proposed algorithm is applied on F3 block dataset, and BBO selects 20 predominant features among 75 features. The optimal solution after 50 generations enables the classification of chimneys through multilayer feed-forward supervised neural network. The results indicate that the chimney classification accuracy is improved by 90%, and the mean square error is minimized with BBO as a feature selection algorithm compared to other evolutionary algorithms.

The idea is targeted at controlling a security system which is integrated with intelligent bikes to decrease the chance of two -wheeler theft. The proposed system is executed using Raspberry pi and Face Recognition based on machine learning. The bike will contain a camera. Camera will be used for face recognition and the scanned face will get matched with a dataset, if it matches the vehicle, get started by using the ignition process. Using the microprocessor we will comparing the face with already saved faces by Pre -Trained Users Face Datasets, if the details matches then the access will be given otherwise it will not give the access on the other hand it will also alert us by buzzer and also the server will send image of person who trying to start our bike through our mail. Also, we have attached a buzzer to alert us at night time because nowadays petrol theft is also taking place everywhere to avoid that, we have inserted switch in a bike, whenever we are not using a bike, that time we will ON switch, it will give an alert sound to us. Vehicle ignition control can be done by using a control relay.

Television is becoming an important part of our everyday life as a mass medium for communication. With its dramatic and demonstrative strength, to change it is a true source of knowledge, education and entertainment. It is convincing to take into account the promotion of social values and norms in a civilized society. The structure and shape of the shape play a major role. Here, we propose a new method called multi-class classification with the help of big data analytics. In this study, we have provided a framework for comparing the quality of various classification methods using statistical simulation when individuals belong to one of the two groups that are mutually exclusive. Here we compare naive Bayes classification, multi-layer perceptron classification and decision tree classification as a test case. From the results, we found that the classification accuracy of multi-layer perceptron classification is higher than the other two in analysing the television viewing behaviour. The data for the study is collected through a direct questionnaire survey. Here, accuracy is assessed over correctly and incorrectly categorized instances. The analysis found that MLP is the best algorithm to predict TV viewing behaviour.

In recent decades the health monitoring system extends with the new technology in International standards with funds in the poor countries. E-health monitoring lags in transferring the insufficient data security and huge medical records. The high power devices required for network function becomes the high cost devices. The limitation for the data insecurity, insufficient bandwidth and high power devices leads a way to the hybrid wide area network uses low power devices. The present work uses the E-Health monitoring system with devices working with low power in the hybrid large range network. The channel optimization algorithm controls the channel data rate and bandwidth. The optimization process enables the transmission for the medical records, which stored in database system. The medical records are transmitted based on the request from the patients and the doctor with the help of Artificial Intelligence (AI). The security system and their data base enables the security for e- health monitoring system. The proposed system improves in high speed, optimized data rate for medical data, low power devices. The system performance compared with the existing 3G e- health monitoring system.

Stage 5 retinopathy of prematurity is a difficult condition to treat despite technological advances in vitreous surgery. A retrospective chart review of all consecutive cases of stage 5 ROP was performed between December 2016 and December 2018, and 21 babies were included for assessment of surgical outcomes using a modified vitrectomy technique. Data extracted from documents included demography, ROP screening status, preoperative prophylactic therapy, clinical presentation, surgery performed, and postsurgical outcomes. Out of the 21 babies, ophthalmologist screening was done in 42.9%. Mean birth weight was 1185 ± 222.4 g with a mean gestational age of 29.86 ± 2.0 weeks and mean post-menstrual age of 44.55 ± 9.82 weeks. Lesser than stage 5 disease was seen in 16.7% of eyes and they were managed accordingly. Seventy percent of babies had bilateral disease. 21 eyes underwent 25-gauge pars plicata vitrectomy using a modified technique. After an average follow-up duration of 6.33 ± 2.18 months, the final macular attachment rate was 19%. Anteriorly closed-posteriorly closed type configuration of retinal detachments had a poorer outcome. Fix and follow visual acuity was achieved in 23.8% of eyes, while 57.1% of eyes had a perception of light. Management of stage 5 ROP is mostly surgical; however, the risk of ending up with a poor vision or vision loss is high, irrespective of whether surgery is performed. The modified surgical technique with a spacer described in this study may help in better manipulation of instruments inside the vitreous cavity.

Recognizing body movements are vital for fields like virtual reality, human computer interaction, and safety monitoring. The research seeks to overcome the limitations of existing body movement recognition systems, which include individual human body recognition rather than multiple recognition and also fails when the body is partially visible. Since most of the real-world applications, like accident monitoring requires recognition of multiple people; at the same time, the existing methodology has reached an impasse. Classification algorithms are predominantly used to recognize various body movements and actions. Even Kinect, which is well known for recognizing human actions and movements, uses a random forest algorithm to track the movements. With recent technological advancements, researchers are keen to increase the accuracy of tracking the movements. New research methodologies seek to overcome the above limitations by using deep neural networks and many other machine learning algorithms. With the inspiration of the success of convolution neural networks (CNNs), the implementation explores usage of the mentioned deep learning algorithm in estimating human poses and recognizes corresponding actions. The idea is to build a scalable model that can be used for many applications and get accurate results as much as possible.

In recent years, sentiment or emotion analysis has become a key research area due to its vast potential applications in getting insights from social media comments, marketing, political science, psychology, human-computer interaction, and artificial intelligence. Emotion analysis deals with identifying the emotions in any given data such as text, speech, or image. The current work proposes to identify and associate social media text to multiple emotions with varying degrees. The data collection and annotation process employed in the proposed work is a combination of manual and semi-supervised annotation method where each tweet is mapped to a six dimensional emotion vector. Totally six human emotions such as happy, sad, anger, disgust, surprise, and fear are considered for emotion-tagging. Word mover‘s distance (WMD) based on twitter word embeddings (word2vec) is proposed to develop a labelled dataset in the current work. A set of classifiers is developed on the labelled dataset to identify emotions at the tweet-level in any given text data. In the current work, KNN, tree-based, and neural network classifiers are developed.

AIMS: This study aims to determine the sensitivity and specificity of vision screening by school teachers among primary school children. SETTINGS AND DESIGN: Prospective nonrandomized clinical trial. SUBJECTS AND METHODS: The study was carried in primary school children of Madurai, Tamil Nadu from April 2007 to October 2007. Sixty-five primary school teachers from 57 schools around Madurai, Tamil Nadu, India, underwent a half-day training programme at the hospital. Each teacher on return to the school screened around a hundred children using the tumbling E. All screened children were subsequently screened by a team from the hospital. The optometrists used tumbling E (Snellen's 20/30 and Early Treatment of Diabetic Retinopathy Study (ETDRS) 20/32) to check the vision of all children. Any child complaining of defective vision or noted to have defective vision on screening underwent a comprehensive ophthalmic evaluation. The sensitivity and specificity of teacher screening was assessed based on the data obtained. RESULTS: Screening was completed for 5150 children. The prevalence of vision

Machine translation is the core problem for several natural language processing research across the globe. However, building a translation system involving low-resource languages remains a challenge with respect to statistical machine translation (SMT). This work proposes and studies the effect of a phrase-induced hybrid machine translation system for translation from English to Tamil, under a low-resource setting. Unlike conventional hybrid MT systems, the free-word ordering feature of the target language Tamil is exploited to form a re-ordered target language model and to extend the parallel text corpus for training the SMT. In the current work, a novel rule-based phrase-extraction method, implemented using parts-of-speech (POS) and place-of-pause in both languages is proposed, which is used to pre-process the training corpus for developing the back-off phrase-induced SMT. Further, out-of-vocabulary (OOV) words are handled using speech-based transliteration and two-level thesaurus intersection techniques based on the POS tag of the OOV word. To ensure that the input with OOV words does not skip phrase-level translation in the hierarchical model, a phrase-level example-based machine translation approach is adopted to find the closest matching phrase and perform translation followed by OOV replacement. The proposed system results in a bilingual evaluation understudy score of 84.78 and a translation edit rate of 19.12. The performance of the system is compared in terms of adequacy and fluency, with existing translation systems for this specific language pair, and it is observed that the proposed system outperforms its counterparts.

Parkinson’s disease (PD), an intensifying neurological disorder is predominantly because of failing dopaminergic neurons of the midbrain. Dopamine is involved in sending of messages to those parts that controls coordination and movement in brain. With the help of Machine Learning approaches, it sets a base for an Intelligent system that helps in computer-aided diagnosis of PD patients. Machine Learning is used for early diagnosis and prediction so that it can be utilized to treat the disease quicker. In medicinal science, it is visible that outputs from the imaging devices can be incorporated for predicting a disease better. The paper specifies a brief synopsis of Machine Learning techniques along with MRI data which can yield faster prediction of PD.

Introduction: Pre-eclampsia is one of the common pregnancy related hypertensive disorders often accompanied with proteinuria with or without oedema. It is associated with risk of preterm labour, Intrauterine Growth Restriction (IUGR), perinatal and maternal mortality. The development of preeclampsia is believed to be multifactorial; however the exact aetiopathogenesis of pre-eclampsia remains debatable. The development of early predictive markers for timely prediction/ detection, orderly management and prevention of the disorder can improve overall outcome. Aim: To determine the diagnostic efficacy of serum cystatin C as a biomarker of renal health in pre-eclampsia and to compare it with conventional renal parameter creatinine. Materials and Methods: This case-control study was conducted in the Department of Biochemistry, Raja Rajeswari Medical College and Hospital, Bengaluru, Karnataka, India. The study group included 50 pre-eclamptic women aged 18- 35 years and 50 age-matched healthy pregnant women. Serum cystatin C was estimated by immunoturbidimetry method and serum creatinine by Isotope Dilution Mass Spectrometry (IDMS) traceable Jaffe’s kinetic method. The data were analysed using unpaired Student’s t-test, p

Background: Superficial mycoses are common worldwide. Dermatophytic infections can greatly affect quality of life. Several newer antimycotic agents, have been reported effective and safe. Hence this study was planned to analyse effectiveness as well as cost effectiveness of these treatments.Methods: It were a prospective, randomized, parallel, open label, comparative study. Fifty patients were included in the study and divided into 2 groups. They were randomized to receive either oral terbinafine 250 mg or itraconazole 100 mg once daily for 4 weeks. Scaling, erythema and pruritus were rated as clinical score 0 to 3: 0 - absent, 1 - mild, 2 - moderate, and 3 - severe for the above three target symptoms. Total symptom score was assessed. Pruritus was also graded on visual analogue scale (VAS). Mycological cure was assessed by skin scraping with KOH mounts and fungal culture. Clinical efficacy scoring and VAS were assessed before the study and at each follow up visit at 2 and 4 weeks. Patients were followed up for another 4 weeks after completion of the treatment.Results: There was highly significant decrease p

A social network (SN)permits the people with same interests to distribute the information, photos and videos. The person who connects on SN sites as personal effort interacts through different media to discuss the interests. The social networks are employed for familiar interaction. A virtual community is a SN with group of individuals who communicate through social media, crossing the geographical and maintaining the boundaries to track the mutual interests. Virtual communities are used for different social and professional groups where communication between community members changes from one individual to another. But, the existing techniques failed to group the similar behavior peoples to form the virtual community. In order to address the problems, the machine learning and ensemble learning techniques are introduced to form the virtual community based on influencing, suspicious and trust behavior with better classification accuracy and lesser time consumption.

The Ant Colony Optimization (ACO) routing technique is an adaptive and reliable approach to find the paths in routing for Mobile Ad hoc Networks (MANETs). In ACO algorithms, the ant as agents traverses across the network to find the shortest path from the source to its destination. During the traversal, the ants deposits pheromones on its path. The path with high probability of pheromone is chosen as the optimized path to transfer data packets between the source and the destination. The quantity of the pheromone laid down on a path depends on its quality metrics viz. minimum number of hops, minimum energy path cost and packet transfer delay. Due to the high dynamic nature of MANETs, the path with high probability of the pheromone deposition may rapidly become unavailable due to link failures. Therefore, it is a challenging task in MANETs to construct a reliable path that is less likely to be disconnected for a long period of time. In this paper, a novel robust energy efficient ACO routing algorithm named AntHocMMP has been proposed. This algorithm is meant to achieve robustness of paths for reliable communication with adaptive re-transmission delays in MANETs. This proposed algorithm enhances the performance of Max---Min---Path (MMP) approach by using ant as agents to find the optimal path in the network. The selection of the optimal path is based on the residual energy of the nodes in that path. A robust route with minimum energy path cost with a short hop-count is chosen for pheromone deposition. Therefore, to keep the paths connected in a dynamic topology, the proposed AntHocMMP redistributes the pheromone based on the energy path cost and it constructs an alternative path for reliable communication between the source and the destination nodes. In this paper, a study has also been done on the effects of re-transmission delays spent in reliably delivering the packets to the destination nodes. The simulation result shows that AntHocMMP achieves a good packet delivery ratio in a wider communication range than the existing algorithms viz. R-ACO1, LAR, MMP and AntHocNet routing algorithms. The statistical analysis viz. t test and Anova-test have been performed that proves which in turn the proposed AntHocMMP dominates the other existing algorithms in a more significant way.

For any given mixed-language text, a multilingual synthesizer synthesizes speech that is intelligible to human listener. However, as speech data are usually collected from native speakers to avoid foreign accent, synthesized speech shows speaker switching at language switching points. To overcome this, the multilingual speech corpus can be converted to a polyglot speech corpus using cross-lingual voice conversion, and a polyglot synthesizer can be developed. Cross-lingual voice conversion is a technique to produce utterances in target speaker's voice from source speaker's utterance irrespective of the language and text spoken by the source and the target speakers. Conventional voice conversion technique based on GMM tokenization suffer from degradation in speech quality as the spectrum is oversmoothed due to statistical averaging. The current work focuses on alleviating the oversmoothing effect in GMM-based voice conversion technique, using (source) language-specific mixture weights in a multi-level GMM followed by selective pole focusing in the unvoiced speech segments. The continuity between the frames of the converted speech is ensured by performing fifth-order mean filtering in the cepstral domain. For the current work, cross-lingual voice conversion is performed for four regional Indian languages and a foreign language namely, Tamil, Telugu, Malayalam, Hindi, and Indian English. The performance of the system is evaluated subjectively using ABX listening test for speaker identity and using mean opinion score for quality. Experimental results demonstrate that the proposed method effectively improves the quality and intelligibility mitigating the oversmoothing effect in the voice-converted speech. A hidden Markov model-based polyglot text-to-speech system is also developed, using this converted speech corpus, to further make the system suitable for unrestricted vocabulary.

Machine translation (MT) is an important aspect of natural language processing (NLP) which uses bilingual data set and other language assets to translate text from a source language to text in a target language. The widely used statistical machine translation (SMT) system mainly relies on translation memories and glossaries to learn language pattern and translation rules. However the usage of these SMT systems under low-resource conditions remain a challenge. These SMT systems have a higher output quality when trained using domain specific training data since the texts belonging to same domain follow same pattern or usage of words. The current paper aims to develop and improve the performance of an SMT system under low-resource conditions for translation of health domain specific text from Tamil-to-English and English-to-Tamil. The translation quality of the machine translation system is improved using tuning based on minimum error rate training (MERT) and a novel neural-inspired sentence generator as a post-processor. The quality of translation and its performance analysis is evaluated in terms of the bilingual language understudy (BLEU) score and translation edit rate (TER).

Nowadays, mobile devices are used for various applications. Although mobile can perform many things, it is a resource restriction device (i.e.) facilities and processing speed are limited. While executing high computational tasks, processing time is high in mobiles due to its sequential task processing algorithm. Computational time of an application depends upon the number of machines (Virtual) involved. Virtualization in Cloud computing is an emerging concept (field) which provides solutions for many complex problems. By combining the merits of virtualization in mobile computing, overall efficiency can be increased. Computationally intensive heavy tasks can be offloaded to rich resourceful server which meets its requirements for time efficient processing. Offloaded tasks are time efficiently processed using virtualization in cloud computing where many VMs (Virtual Machines) are used to dynamically complete the given application using suitable algorithm. The main objective of this paper is to find optimized mapping between number of tasks offloaded, and virtualization in cloud resource. The proposed approach is implemented in Workflowsim which is a cloud resource simulator, and the data obtained is statistically analysed for optimized result.

Particle board serves numerous functions such as cabinetry, tabletops, shelving, wall & floor panels, etc. Though Particle boards are much cheaper compared to solid wood, machining the material by optimizing the process parameters is a big challenge. Drilling is the major operation during final assembly of particle board, where fasteners are used to join together to bring final shape to the product. In this paper, the operation parameters such as speed, feed, drill diameter has been considered during drilling operation performed on Particle board and the output responses-surface roughness of the drilled hole and the Thrust force acting on the board during drilling are measured. Good surface finish of the hole ensures reduction of localised stresses around the hole due to fastening. Reduced Thrust force contributes to lesser delamination in particle board. Hence minimisation of both surface roughness and delamination is desired and hence optimisation of both responses is done simultaneously using grey relational analysis.

Medium density fiber board (MDF) is an engineered wood product which possesses medium density than normal wood and is widely applicable in interior and exterior applications in industries. Normally in drilling process the materials reduce their performance due to delamination. The objective of this paper is to estimate delamination effect on MDF board. The modeling was done using ANFIS tool box of MATLAB. The experiment was done by giving the input parameters- speed, feed rate, drill diameter for which the output response taken was delamination. Root mean square error for different membership functions of delamination was obtained and the effect of speed, feed rate and drill diameter was studied.

Nitrogen (N) plays an important role in plant growth, development and also one of the major factor for developing a high-yielding rice cultivars. Nitrogen use efficiency (NUE) in plants is a complex phenomenon that depends on a number of internal and external factors, which include soil N availability, its uptake and assimilation of carbon and nitrogen. An increased awareness of the regulatory mechanisms controlling Nitrogen economy is imperative to enhance nitrogen uptake and use efficiency so as to reduce excessive input of fertilizers, while maintaining an acceptable yield. The physiological, biochemical, molecular aspects like QTL, mi RNA technology and transgenic approaches as well as NUE can be targeted to improve rice productivity. Yield being complex and multigenic trait linkages between carbon and nitrogen pathways are essential. An attempt on complex interactions between the two major physiological pathways linked by photosynthesis and photorespiration in global climate change for enhancing NUE in relation to rice yield was reviewed.

This paper describes a speech data collected from 22 dysarthric speakers (7-female & 15-male) of various age groups in an Indian language, namely, Tamil. Dysarthric speakers who reported a diagnosis of cerebral palsy are chosen for speech data collection. The text for recording is chosen such that the influence of the articulatory errors can be observed in the initial, medial and end of the word. Each dysarthric speaker has uttered 262 sentences and 103 isolated words using a head mounted microphone. This corpus includes clinical data for all the 22 dysarthric speakers. The entire corpus is marked in sentence and word level. Time-aligned phonetic transcriptions are available for all the 22 speakers. The phonetic transcriptions are mapped with the IPA for a global use. The speech corpus is designed to act as a resource for the development of an automatic speech recognition system for speakers with neuromotor disability, research on articulatory dysfunctions, and rehabilitation research. The corpus includes unimpaired speech data, collected from 10 speakers (5-male & 5-female) for the same text used to collect dysarthric speech data along with the time-aligned phonetic transcription, for comparison. The corpus is available, on request, via secure FTP.

Machine Translation systems are fast replacing human translators to aid us in translation between any pair of natural languages. Conversational sentences form the fundamental structure of a natural language. These sentences help in understanding the basic conversations in a language and hence improving translation of these sentences is considered more essential to improve the quality of a good translation systems in various real-life applications. This paper focuses on machine translation for travel conversations between any pair of languages namely, English, Hindi, and Tamil, with minimal intervention of linguistics. The translation system is trained using various text corpus sizes (1K, 5K, 13K sentences) of conversational and complex sentences for comparison. To improve the performance of a machine translation system a language identification system coupled with post-editing approach that applies n-best translation list analysis and language models are used. An improvement of translation performance by 3–5% in bilingual evaluation understudy (BLEU) score is observed without incorporating linguistic information.

The aim of this work is to develop a wearable device for the safety and protection of women and girls. This objective is achieved by the analysis of physiological signals in conjunction with body position. The physiological signals that are analyzed are galvanic skin resistance and body temperature. Body position is determined by acquiring raw accelerometer data from a triple axis accelerometer. Acquisition of raw data is then followed by activity recognition which is a process of employing a specialized machine learning algorithm. Real-time monitoring of data is achieved by wirelessly sending sensor data to an open source Cloud Platform. Analysis of the data is done on MATLAB simultaneously. This device is programmed to continuously monitor the subject's parameters and take action when any dangerous situation presents itself. It does so by detecting the change in the monitored signals, following which appropriate action is taken by means of sending notifications/alerts to designated individuals

Human beings interact with each other to convey their ideas, thoughts, and experiences to the people around them. But this is not the case for deaf-mute people. Sign language paves the way for deaf-mute people to communicate. Through sign language, communication is possible for a deaf-mute person without the means of acoustic sounds. The aim behind this work is to develop a system for recognizing the sign language, which provides communication between people with speech impairment and normal people, thereby reducing the communication gap between them. Compared to other gestures (arm, face, head and body), hand gesture plays an important role, as it expresses the user's views in less time. In the current work flex sensor-based gesture recognition module is developed to recognize English alphabets and few words and a Text-to-Speech synthesizer based on HMM is built to convert the corresponding text.

Language in any form is the fundamental requirement to communicate and interact within the human society. In this globalization era, we interact with people from different regions and linguistic backgrounds as per our interest in social, cultural, economical, educational and professional domain. It is quite tough, rather impossible to know all the languages. Thus we need a computerized approach to convert one natural language to another as per the necessity. In this paper, we discuss statistical machine translation for the languages Tamil and English limited to travel domain. The system aims to translate from English to Tamil and vice versa. GIZA++ tool is used for training the statistical models. The performance of the system is analyzed based on various performance metrics like BLEU score and TER.

The option of electro-oxidation and advanced oxidation as tertiary treatment technique for the purification of tannery wastewater was explored. The TOC removal of 85% was achieved by UV/O3/H2O2 process, whereas it is hardly 50% by electr-ooxidation. However the power consumption to remove unit mass of TOC by electro-oxidation process was estimated to be 738 kW h/kg, which is ten times less than that of 7600 kW h/kg, required for advanced oxidation process. The kinetic data indicated that the degradation of organics by electro-oxidation is a current control process. To minimize the power consumption, we attempted a two-stage process involving electro-oxidation in the first stage and advanced oxidation in the second stage. The results indicated that the TOC removal by advanced oxidation became sluggish, when the wastewater was processed initially by electro-oxidation. However, the effluents processed by EO were found to be completely disinfected.

Mobile Ad hoc network is self-organizing and adaptive in nature. An Ad hoc wireless network does not rely on any fixed network entities the network itself is essentially infrastructure less. However, due to the presence of mobility, routing information changes to reflect subsistence in link connectivity. This paper highlights the performance of routing protocols like AODV, DSDV, DSR and TORA based on various evaluating parameter metrics like routing overload, throughput, average end-to-end delay, Packet Delivery Fraction (PDF) etc., by increasing the number of nodes when nodes are in mobile and tend to route the packets from source to destination. The simulation analysis proves that these metrics vary with different values in different test scenarios by diversifying the nodes.

Energy efficient hint based routing protocol (EEHP) in wireless networks is the problem of finding energy efficient shortest active path which also senses the node breakage before occurrence of disconnection and performs a handoff. In the existing Hint Based Routing Protocol (HBP), the nodes discover an active path to the destination exploiting a set of routing meta-information (called hints). The active paths based on hint computation is alone not sufficient for nodes in the ad hoc network for which there may be energy loss during packet transfer leading to disconnection. to avoid path loss a new energy efficient hint based routing approach outperforms the existing HBP protocol by inheriting the node handoff strategy into the HBP protocol which finds the active shortest path based on hint computation followed by route maintenance based on power of the nodes in the active route. The simulation results show that the proposed protocol performs well when a link failure occurrence is identified preventing the route from being broken, leading to energy efficient and better performance in the network.

Major constrain in Acoustic communication network is the consumption of energy by senor nodes. The need to regulate the energy con-sumption of senor nodes relay mainly on underwater applications like disaster prevention, tsunami warning and other environmental moni-toring, where the sensor need to transmit the sensed data constantly to the control station for critical analysis. This paper mainly focuses in designing a routing protocol that can challenge a better life expectancy of underwater sensor nodes. From the broader area of research in UWSN, VBF routing protocol gives a promising performance in term of increase in data throughput, PDR and energy consumption. Hence an energy efficient vector based forwarding protocol (EEVBF) is designed which can efficiently regulates the energy consumption when compared to VBF. Based on simulation results, the performance analysis of data throughput, packet delivery ratio, and energy consumed using aquasim network simulators.

This article solves the customer complaint of poor performance of brake in certain tractors. This failure was reported particularly in Tractor which is predominantly used for puddling application. After prolonged usage, breakage was occurring in certain area of the actuating link. In order to overcome this problem, analysis has been carried out by using finite element method along with optimization technique. To obtain the best result various link designs have been created in CATIA and optimized with an optimization tool called OptiStruct solver in Hypermesh. The stress and deflection plots of existing and modified link were compared. Then the fatigue life of the optimized model is obtained from Nastran in order to know the life of the actuating link. In the same way fatigue life of the existing and modified link were analyzed and discussed.

Speech-to-speech translation system enables in translation of speech signals in a source language A to target language B. A good speech-to-speech translation (S2ST) system can be characterized by its ability to keep intact the fluency and meaning of the original speech input. An S2ST system to enable translation between Hindi and English is the main idea of the proposed work. A preliminary dataset concentrating on basic travel expressions in both the languages considered is used for this work. In order to develop a successful S2ST system three subsystems are required namely, automatic speech recognition (ASR) system, machine translation (MT) system and text-to-speech synthesis (TTS) system. Hidden Markov models based ASR system is developed for both the languages and their performances are analyzed based on the word error rate (WER). The MT subsystem makes use of the statistical machine translation (SMT) approach for the purpose of translating the text between the two languages involved. The SMT makes use of IBM alignment models and language models to enable proper translation. The performance of MT is analyzed based on translated edit rate (TER) and analysis of the translation table. HMM-based speech synthesis system (HTS) is used to synthesize the translated text. Performance of the synthesizer is analyzed based on mean opinion score (MOS) from a group of listeners.

In traditional ART2 network, class representation is contained in a F2 node instead of being dispersed on several nodes. As a result, any harm to that F2 node leads to the loss of that class. The proposed network is capable of maintaining categories in a dynamic hierarchical structure. The new architecture is characterized by: (a) maintains all the characters of traditional ART2 network; (b) Creates better clustering of patterns; (c) preserve learnt patterns in a dynamic structure. The proposed networks provide a few advantages, quick learning and reduced search time. To demonstrate their capabilities, the proposed networks were applied to solve the image classification problems. The proposed network is believed to achieve effective unsupervised task and it has been experimentally found to perform well in images classification.

This paper examines the nature and source of gender variations in Internet use and how cultural factors contribute to such variations. The study addresses the following issues: gender variations in the depth and width of Internet adoption; gender variations and online relationships; gender variations and online discourse; identity and anonymity in online relationship and gender variations; cultural factors contributing to gender variations. The study was conducted in three towns of Andhra Pradesh. Different methods were adapted to collect, analyse and represent the data. A survey was conducted on 450 men and 450 women to assess the gender variations in the width and depth of Internet adoption and usage. The study provides insight into the gender variations in using the Internet space and forming online relationships, and online discourse. On the whole, the study supports the fact that the Internet provides an opportunity for women to construct their identities, challenge certain traditional norms ...

Background: Adolescent girl’s knowledge about sexual abuse and awareness of health services is poor. The study is conducted to analyse the gynaecological problems mandating hospitalization and to formulate modalities to improve their health and to ensure safe motherhood. The objectives of this study were to evaluate gynaecological problems in adolescent girls, and to analyse gynaecological emergencies, and to develop modalities to improve adolescent health and for safe motherhood.Methods: Retrospective hospital based observational study of hospital records regarding health issues and awareness of health services among girls in the age group of 13-19 years hospitalized for various medical and surgical problems during the period of January 2015 to January 2016 at Shri Sathya Sai medical college and Research Institute, Ammapettai, Kancheepuram, Tamil Nadu, India. 50 age, education, social background, knowledge of available health services, contraception and detailed history of menstruation regarding duration, quantity and previous medication prior to admission and their present cause of admission were analysed.Results: Total admission in the year were 50, medical 34 (including 5 girls who had check curettage for postabortal bleeding with severe anemia) and surgical 16. Early adolescent group were 34 (68%)and late adolescent group were 16 (32%), married were 5 (10%) and unmarried 45 (90%). All 50 girls were from low socioeconomic class, with rural background. 84% were school dropout and 40% were unemployed. 20 % of our study subjects were admitted with unsafe abortion with postabortal bleeding with no knowledge about Health services.16% were sexually abused and were raped. Abnormal uterine bleeding(menorrhagia) is the most common gynaecological problem in adolescent girls in our study. DUB (52%) is the most common cause. Second most common cause for abnormal bleeding is postabortal bleeding (40%). The most common surgical procedure done was check curettage for postabortal bleeding in 10%. Next common surgery was laparotomy for ovarian cyst in 6% cases and for torsion adnexal cyst in 6%.Conclusions: The most important cause for admission was anemia following abnormal excessive bleeding P/V due to DUB or postabortal bleeding. Second most important cause was ovarian tumours. Unprotected coitus, sex abuse and repeated unsafe abortions have increased the rate of PID and ectopic pregnancies. The study emphasizes the need to sensitize these young girls on the issue like excessive menstruation, gender relationships, and sex abuses. Create awareness and promote knowledge regarding medical issue, available health services and contraception to prevent unsafe abortion and its sequelae. Active implementation of projects like ARSH - adolescent friendly reproductive services, where confidentiality is maintained and proper counseling regarding contraception and supplementation of iron and folic acid is given to all adolescent girls who are future mothers and to ensure safe motherhood.

A number of ocular complications have been reported in microspherophakia. The literature however is limited to small case reports and the incidence of these complications is largely unknown. Our study describes a series of patients who presented to our hospital from 1998 to 2008. Data on the clinical and surgical findings of patients presented to us from 1998 to 2008 with microspherophakia were retrieved from the medical records and the results analyzed. Thirty-six eyes of 18 patients were reviewed. The mean age at presentation was 16±10 years. All patients had varying degrees of lenticular myopia with a mean of −11.07±5.03 D. Glaucoma developed in 16 eyes (44.4%). Half of them had high IOP at presentation. Despite medical and surgical management IOP remained high in five eyes at the last follow-up. Sixteen eyes (44.4%) required lensectomy for dislocated crystalline lens. Lensectomy did not have any impact on the intraocular pressures. Homocysteinuria was the most common systemic association noted. Microspherophakia is associated with a high incidence of lenticular myopia, subluxation of the crystalline lens and glaucoma. Management of glaucoma is difficult with the IOP remaining high in spite of combined medical and surgical management.

Text-to-speech synthesis systems are expected to produce speech that is intelligible and natural. While conventional systems are capable of producing highly intelligible speech, naturalness needs to be improved, in the sense that regardless of the context, any given text is synthesized in a neutral tone. A number of existing techniques to synthesize emotional speech are data driven. However, collecting a large amount of emotional data is tedious. Therefore, signal processing algorithms can be used to modify neutral speech. The current work concentrates on incorporating happiness into neutral speech. Analysis reveals that happiness in speech primarily affects the pitch contour and the intensity of speech, and variations in these features are predominantly observed only in the emotive-keywords. Therefore, in the current work neutral speech is transformed to happy speech, by using signal processing algorithms to modify the pitch and intensity of the emotive-keywords. The happy speech synthesized by the proposed method, when assessed subjectively, yields a mean opinion score of 2.53 out of a possible 3. The synthetic speech is also assessed objectively using a GMM-based emotion recognition system, and all the tested sentences are recognized to be happy.