An investigation of the breast cancer classification using various machine learning techniques

It is an extremely cumbersome process to predict a disease based on the visual diagnosis of cell type with precision or accuracy, especially when multiple features are associated. Cancer is one such example where the phenomenon is very complex and also multiple features of cell types are involved. Breast cancer is a disease mostly affects female population and the number of affected people is highest among all cancer types in India. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using cell image processing. Under these pattern recognition techniques, cell image segmentation, texture based image feature extraction and subsequent classification of breast cancer cells was successfully performed. When four different machine learning techniques: Kth nearest neighbor (KNN), Artificial Neural Network ( ANN), Support Vector Machine (SVM) and Least Square Support Vector Machine (LS-SVM) was used to classify 81 cell images, it was observed from the results that the LS-SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 95.3488% among four other classifiers while SVM with linear kernel resulted a classification rate of 93.02% which was close to LSSVM classifier. Thus, it was demonstrated that the LS-SVM classifier showed accuracy higher than other classifiers reported so far. Moreover, our classifier can classify the disease in a short period of time using only cell images unlike other approaches reported so far

Artificial Neural Network based Body Posture Classification from EMG signal analysis

 This paper deals with the body posture Classification from EMG signal analysis using artificial neural network (ANN). The various statistical features extracted from each EMG signal corresponding to different muscles associated with the different body postures are framed using LABVIEW software. Further-more, these features are taken as the input towards the ANN classifier and thus the corresponding output for the respective classifier predicts the postures like Bowing, Handshaking, and Hugging. The performance of the classifier is determined by the classification rate (CR). The outcome of result indicates that the CR of Multilayer Feed Forward Neural Network (MFNN) type of ANN is rounded up to a percentage of 71.02%

Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model’s performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using time-frequency images of multi-sensor data. Finally, the proposed model’s performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier.publishedVersio

Identification of seed storage protein markers for drought tolerance in mungbean

A set of 292 mungbean germplasm accessions including 62 popularly adapted local land races and two wild forms (Vigna radiata var. sublobata), important breeding lines and standard ruling varieties were screened for drought stress tolerance at seedling stage.  Eight genotypes e.g., C. No. 35, OUM 14-1, OUM 49-2, Pusa 9072, OM 99-3, Banapur local B, Nipania munga, Kalamunga 1-A) have been identified to possess drought tolerance.  Globulin seed storage protein profiling was carried out in 19 selected mungbean genotypes comprising eight drought tolerant, seven drought sensitive, two wild forms of mungbean (TCR 20 and TCR 213) and two standard checks (LGG 460 and T 2-1) to explore differentially expressed polypeptides. Seed protein profiles revealed 15 scorable polypeptide bands with molecular weights ranging from 10.0 to 102.2kD. A specific 12.8kD polypeptide band was present in all above drought tolerant test genotypes including the wild accession TCR 20. Such a polypeptide band may serve as useful biochemical marker for identification of drought tolerant genotypes in mungbean.             Key words: Genetic diversity, seed storage protein profile, wild and cultivated Vigna radiata

Globulin seed storage protein based genotyping and Study of genetic diversity in core accessions of mungbean under drought stress

Globulin seed storage protein profiles of 19 mungbean genotypes including two wild forms of Vigna radiata var. sublobata(TCR 20 and TCR 213) and two standard  checks(T 2-1 and LGG 460) were analysed by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Thirteen genotypes could be clearly identified based on genotype-specific seed protein fingerprints. The combined dendrogram showed six genetic clusters within 68% phenon level. The clustering based on the combined clustering analysis revealed discrimination of all test genotypes even immediately beyond 88% phenon level, whereas individual clustering analysis based on protein and agro-morphological level failed to do so. Nipania munga, TCR 213, T 2-1, LGG 460, TCR 20 and Banapur local B were identified to be highly divergent genotypes. TCR 20 appears to have more genetic proximity to the mungbean genotypes than TCR 213. T 2-1, LGG 460 and TCR 20 are potentially high yielding. These may serve as valuable materials for recombination breeding in mungbean

Eff ect of participatory women’s groups facilitated by Accredited Social Health Activists on birth outcomes in rural eastern India: a cluster-randomised controlled trial

Background A quarter of the world’s neonatal deaths and 15% of maternal deaths happen in India. Few community-based strategies to improve maternal and newborn health have been tested through the country’s government-approved Accredited Social Health Activists (ASHAs). We aimed to test the eff ect of participatory women’s groups facilitated by ASHAs on birth outcomes, including neonatal mortality. Methods In this cluster-randomised controlled trial of a community intervention to improve maternal and newborn health, we randomly assigned (1:1) geographical clusters in rural Jharkhand and Odisha, eastern India to intervention (participatory women’s groups) or control (no women’s groups). Study participants were women of reproductive age (15–49 years) who gave birth between Sept 1, 2009, and Dec 31, 2012. In the intervention group, ASHAs supported women’s groups through a participatory learning and action meeting cycle. Groups discussed and prioritised maternal and newborn health problems, identifi ed strategies to address them, implemented the strategies, and assessed their progress. We identifi ed births, stillbirths, and neonatal deaths, and interviewed mothers 6 weeks after delivery. The primary outcome was neonatal mortality over a 2 year follow up. Analyses were by intention to treat. This trial is registered with ISRCTN, number ISRCTN31567106. Findings Between September, 2009, and December, 2012, we randomly assigned 30 clusters (estimated population 156 519) to intervention (15 clusters, estimated population n=82 702) or control (15 clusters, n=73 817). During the follow-up period (Jan 1, 2011, to Dec 31, 2012), we identifi ed 3700 births in the intervention group and 3519 in the control group. One intervention cluster was lost to follow up. The neonatal mortality rate during this period was 30 per 1000 livebirths in the intervention group and 44 per 1000 livebirths in the control group (odds ratio [OR] 0.69, 95% CI 0·53–0·89). Interpretation ASHAs can successfully reduce neonatal mortality through participatory meetings with women’s groups. This is a scalable community-based approach to improving neonatal survival in rural, underserved areas of India

Effect of participatory women's groups facilitated by Accredited Social Health Activists on birth outcomes in rural eastern India: A cluster-randomised controlled trial

Background: A quarter of the world's neonatal deaths and 15% of maternal deaths happen in India. Few community-based strategies to improve maternal and newborn health have been tested through the country's government-approved Accredited Social Health Activists (ASHAs). We aimed to test the effect of participatory women's groups facilitated by ASHAs on birth outcomes, including neonatal mortality. Methods: In this cluster-randomised controlled trial of a community interve

The equity impact of participatory women's groups to reduce neonatal mortality in India: secondary analysis of a cluster-randomised trial.

Progress towards the Millennium Development Goals (MDGs) has been uneven. Inequalities in child health are large and effective interventions rarely reach the most in need. Little is known about how to reduce these inequalities. We describe and explain the equity impact of a women’s group intervention in India that strongly reduced the neonatal mortality rate (NMR) in a cluster-randomised trial. We conducted secondary analyses of the trial data, obtained through prospective surveillance of a population of 228 186. The intervention effects were estimated separately, through random effects logistic regression, for the most and less socio-economically marginalised groups. Among the most marginalised, the NMR was 59% lower in intervention than in control clusters in years 2 and 3 (70%, year 3); among the less marginalised, the NMR was 36% lower (35%, year 3). The intervention effect was stronger among the most than among the less marginalised (P-value for difference = 0.028, years 2-3; P-value for difference = 0.009, year 3). The stronger effect was concentrated in winter, particularly for early NMR. There was no effect on the use of health-care services in either group, and improvements in home care were comparable. Participatory community interventions can substantially reduce socio-economic inequalities in neonatal mortality and contribute to an equitable achievement of the unfinished MDG agenda

Community mobilisation with women's groups facilitated by Accredited Social Health Activists (ASHAs) to improve maternal and newborn health in underserved areas of Jharkhand and Orissa: study protocol for a cluster-randomised controlled trial

Background: Around a quarter of the world's neonatal and maternal deaths occur in India. Morbidity and mortality are highest in rural areas and among the poorest wealth quintiles. Few interventions to improve maternal and newborn health outcomes with government-mandated community health workers have been rigorously evaluated at scale in this setting.The study aims to assess the impact of a community mobilisation intervention with women's groups facilitated by ASHAs to improve maternal and newborn health outcomes among rural tribal communities of Jharkhand and Orissa.Methods/design: The study is a cluster-randomised controlled trial and will be implemented in five districts, three in Jharkhand and two in Orissa. The unit of randomisation is a rural cluster of approximately 5000 population. We identified villages within rural, tribal areas of five districts, approached them for participation in the study and enrolled them into 30 clusters, with approximately 10 ASHAs per cluster. Within each district, 6 clusters were randomly allocated to receive the community intervention or to the control group, resulting in 15 intervention and 15 control clusters. Randomisation was carried out in the presence of local stakeholders who selected the cluster numbers and allocated them to intervention or control using a pre-generated random number sequence. The intervention is a participatory learning and action cycle where ASHAs support community women's groups through a four-phase process in which they identify and prioritise local maternal and newborn health problems, implement strategies to address these and evaluate the result. The cycle is designed to fit with the ASHAs' mandate to mobilise communities for health and to complement their other tasks, including increasing institutional delivery rates and providing home visits to mothers and newborns. The trial's primary endpoint is neonatal mortality during 24 months of intervention. Additional endpoints include home care practices and health care-seeking in the antenatal, delivery and postnatal period. The impact of the intervention will be measured through a prospective surveillance system implemented by the project team, through which mothers will be interviewed around six weeks after delivery. Cost data and qualitative data are collected for cost-effectiveness and process evaluations