Background The optimal surgical strategy for mitral valve (MV) infective endocarditis (IE) remains uncertain. Although valve repair is increasingly advocated, MV replacement is frequently performed, and robust data comparing long-term outcomes between approaches are limited. We evaluated long-term survival following MV repair vs replacement in patients with IE-related mitral regurgitation.Methods We retrospectively analysed 88 consecutive patients who underwent MV surgery for IE-associated mitral regurgitation at St George’s Hospital NHS Foundation Trust, UK, between June 2011 and May 2025. Long-term all-cause mortality was assessed using Kaplan-Meier survival analysis. Multivariable logistic regression identified independent predictors of mortality, and model discrimination was evaluated using the area under the receiver operating characteristic (AUROC) curve.Results The cohort comprised 65% men with a median age of 57 years (IQR 44.0–64.8). MV replacement was performed in 51.1% of patients who were older than those undergoing repair (median age 62 vs 51 years). In-hospital mortality was 4.5% and long-term all-cause mortality was 14.8%. No in-hospital deaths occurred in the repair group. In age-adjusted and sex-adjusted analyses among replacement patients, increasing age (OR 1.1; 95% CI 1.0 to 1.1; p=0.03) and diabetes mellitus (OR 7.8; 95% CI 1.3 to 48.8; p=0.02) independently predicted long-term mortality. The model demonstrated good discrimination (AUROC 0.83; 95% CI 0.69 to 0.97). Mean survival was significantly longer following repair than replacement (161.0 vs 129.9 months; p=0.008).Conclusions MV repair for infective endocarditis is safe and associated with superior long-term survival compared with replacement. Diabetes mellitus is a strong independent predictor of mortality in the MV replacement group, highlighting the importance of risk stratification in surgical decision-making.

BACKGROUND:The ideal harvesting techniques of the left internal mammary artery (LIMA) for coronary artery bypass graft (CABG) are elusive. We assessed the safety and resource utilisation efficiency of semi-skeletonised LIMA harvesting techniques, focusing on length, harvesting time, and the number of Ligaclips used compared to skeletonised techniques within a single surgeon's practice. METHODS:The BANGABANDHU (Bangladeshi Atherosclerosis Biobank AND Hub) study was an ambispective observational cohort that evaluated age- and sex-matched 2209 adult Bangladeshi isolated CABG population from 1st January 2015 to 31 January 2025. Univariate analysis observed the difference pattern in the dataset, while multivariate logistic regression (LR) analysis identified the independent variables associated with the advantage of semi-skelitonised LIMA. The area under the receiver operating characteristic (AUROC) curve demonstrated the goodness-of-fit of the prediction model. RESULTS:We evaluated 2209 age- and sex-matched adult isolated CABG patients (skeletonised LIMA; n = 1050 and semi-skeletonised LIMA; n = 1159) with identical comorbidities (EuroSCORE II, hypertension, diabetes, renal impairment, COPD, left main and multivessel coronary artery disease) between study groups (p > 0.05). LIMA harvest time (35.9 ± 5.5 vs 16.6 ± 3.9; p < 0.001) and number of used Ligaclip (23.6 ± 4.8 vs 11.7 ± 3.6; p < 0.001) were significantly higher in the skeletonised compared to the semi-skeletonised LIMA sample. Furthermore, an age and sex-adjusted multivariate logistic regression model found LIMA harvest time (odds ratio [OR] 0.067, 95% CI 0.01-0.39; p = 0.003) and number of used Ligaclip (OR 0.561, 95% CI 0.41-0.76; p < 0.001) significantly lower among semi-skeletonised LIMA techniques. CONCLUSION:The semi-skeletonised LIMA technique is advantageous as it significantly reduces harvesting time and requires fewer Ligaclips compared to the skeletonised technique.

Background:Postoperative new atrial fibrillation (POAF) commonly occurs after coronary artery bypass graft (CABG) and is often associated with postoperative pericardial effusion. We aimed to investigate the effectiveness of a posterior pericardial window (PPW) with a single left pleural drain in reducing post-CABG pericardial effusion and atrial fibrillation without mediastinal chest drains. Methods:This descriptive observational study evaluated age and sex-adjusted isolated elective on-pump CABG patients into two groups: PPW with only left pleural chest drains and control (routine multiple mediastinal and pleural chest drains. We performed continuous telemonitoring for 96 hours after surgery to assess heart rhythm, followed by daily electrocardiograms. Bedside echocardiography was conducted on postoperative day 4 to check for pericardial effusion. Results:This study evaluated age and sex-adjusted 250 CABG patients, with male predominance (80%) and identical comorbidities between study groups. We found similar age (61.5 ±7.5 vs 62.6 ±6.2, P =0.40) and male sex (86.9% vs 74.8%, P =0.13) between the PPW and control groups. Additionally, the sociodemographic and intraoperative variables were the same across the study groups (P >0.05). The occurrence of >1cm pericardial effusions (0.8% vs 14.1%, P <0.001) and postoperative AF (6.9% vs 19.3%, P =0.002) occurrence was significantly lower in the PPW compared to the control group. Conclusion:Despite similar clinical and operative profiles, a posterior pericardial window with a single left pleural drain effectively reduces pericardial effusion and the incidence of postoperative AF following CABG surgery.

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

Objective:This study determined hazard factors and long-term survival rate of total arterial coronary artery bypass graft surgery over 20 years in an extensively large, population-based cohort.Methods:A total of 2979 patients who underwent isolated CABG from April 1999 to March 2020 were studied in 4 groups- Group-A (bilateral internal mammary artery ± radial artery), Group-B (single internal mammary artery + radial artery ± saphenous vein), Group-C (single internal mammary artery ± saphenous vein; no radial artery), and Group-D (radial artery ± saphenous vein; no internal mammary artery). The study endpoints analysed the correlation between the number and types of grafts with the survival time following isolated CABG surgery.Results:The total arterial revascularization (Group A) group had an admirable mean long-term survival of ~19 years, compared to 18.6 years (Group B), 15.86 years (Group C), and 10.99 years (Group D). A Kaplan-Meier curve demonstrated confidence interval (CI) for study groups- (95% CI 18.33-19.94), (95% CI 18.14-19.06), (95% CI 15.40-16.32), and (95% CI 9.61-12.38) in Group A, B, C, D respectively. In the Holm-Sidak method analysis, significant associations existed between the number of arterial grafts and the long-term outcome. A statistically significant (P≤0.05) long-term survival advantage for arterial grafting was demonstrated, especially total arterial revascularisation over all other combinations except single internal mammary artery + radial artery grafting.Conclusion:In this series, over 20 years, total arterial CABG use has excellent long-term survival, achieving complete myocardial revascularisation. There is no significant difference between the BIMA group and SIMA with radial artery. However, there is a reduced survival with decreased use of arterial conduits.

BACKGROUND:The types of graft conduits and surgical techniques may impact the long-term outcomes of patients after coronary artery bypass graft (CABG) revascularization. This study observed a long-term survival rate following CABG surgery over 20 years in the United Kingdom.METHODS:A total of 2979 isolated CABG patients were studied from 1999 to 2020, and postoperative data were obtained from the hospital-recorded mortality by the data quality team of the information department. Postdischarge survival was estimated using the Kaplan-Meier method, and statistical significance was obtained with log-rank tests and the Gehan-Breslow test, and the Holm-Sidak method was used for multiple pairwise comparisons.RESULTS:The study observed male predominance (80%), and the median age was statistically significant (P <0.001) among the groups, 66 years (interquartile range 58-73) and 72 years (interquartile range 66-78) in survivor and non-survivor groups, respectively. In the Holm-Sidak method analysis, the best survival rate (mean 18.7 years) was observed in the total arterial group with significantly decreased survival for the mixed arterial and venous group (mean 16.12 years) and only the vein group (10.44 years). The Cox regression model observed that the New York Heart Association (NYHA) class III-IV (HR 1.57), chest re-exploration (HR 2.14), preoperative dialysis (HR 3.13), and redo surgery (HR 3.04) were potential predictors of the postoperative mortality (P ≤0.05).CONCLUSION:In our series over 20 years, albeit off-pump and on-pump CABG observed similar survival rates, the total arterial myocardial revascularization population has significantly better long-term survival benefits.

In 1986, Loop et al. first illustrated the long-term prognostic benefits of left internal mammary artery (LIMA) to left anterior descending graft.1 Surgical myocardial revascularisation with coronary artery bypass graft (CABG) surgery is the most commonly performed and preferred strategy for multivessel coronary artery disease.1, 2 In the absence of large enough powered randomized controlled trials (RCTs), recent studies observed that total arterial revascularisation (TAR) utilizing bilateral internal mammary artery (IMA) and radial conduits carry better longevity and reduces postoperative morbidity, particularly in early graft failure, recurrent angina, and redo-CABG surgery.3, 4 However, the potential challenge of TAR-CABG surgery, especially among left main coronary artery disease, depends on the premise that TAR will have a better graft patency rate and postoperative health-related quality of life.4-6 Here, we describe the long-term (≥6 months) survival benefits of myocardial revascularisation with multiple arterial CABG surgery over 20 years in the United Kingdom. A total of 2979 consecutive isolated elective CABG patients at St Georges University Hospital NHS Foundation Trust from April 1999 to March 2020 were studied, and the last day of the census was May 5, 2021. The study population was distributed in four groups—bilateral internal mammary artery + radial (BIMA+R; n = 431), single internal mammary artery + radial ± vein (SIMA+R±V; n = 823), single internal mammary artery − radial ± vein (SIMA−R±V; n = 823), and radial ± vein (R±V; n = 160) groups. The institutional review board clearance was waived as this retrospective analysis of prospectively collected data under the adult National Institute for Cardiovascular Outcomes Research UK database. Study inclusion criteria were isolated CABG with or without prior history of heart surgery, and patients with concomitant valvular, congenital heart diseases were excluded from the study. Multiple arterial graft CABG populations (BIMA+R and SIMA+R±V) have ≥3 arterial grafts, including sequential arterial grafts with the LIMA, right internal mammary artery (RIMA), and radial artery with or without venous grafts. A statistical package for the social sciences 25.0 version software was utilized to analyze the data, and a p value ≤ 0.05 is considered statistically significant. We found that males (~80%) are predominant, and the median age was 61 years (interquartile range [IQR]: 55–68), 63 years (IQR: 57–69), 72 years (IQR: 65–77), and 71 years (IQR: 65–77) in BIMA+R, SIMA+R±V, SIMA−R±V, and R±V groups, respectively. Gender distribution, male versus female, was 90.7% versus 9.3%; 81.2% versus 18.8%; 78.5% versus 21.5%; and 76.3% versus 23.7% among the BIMA+R, SIMA+R±V, SIMA−R±V, and R±V groups, respectively. Multiple arterial CABG (≥3 grafts) was performed in 45.5% and 39.9% cases among BIMA+R and SIMA+R±V populations, respectively. Further, 35.3% and 34.4% of patients had multiple (≥3) mixed arterio-venous grafting in SIMA−R±V and R±V CABG groups. We found that overall survival times were 19.1, 18.6, 15.8, and 10.9 years with BIMA+R, SIMA+R±V, SIMA−R±V, and R±V groups, respectively. Redo CABG was performed in four cases; two cases in each SIMA+R±V (0.2%) and SIMA−R±V (0.1%) group. A statistically significant (p ≤ 0.05) long-term (≥6 months) survival advantage for multiple arterial grafting was demonstrated, especially TAR, over all other combinations except single internal mammary artery + radial artery grafting (Figure 1). This study observed multiple arterial graft CABG population had better long-term survival, and the poorest outcome was in the R±V group with no IMA graft, similar to recently published articles where saphenous vein grafts are more prone to developing early graft failure and develop recurrent angina attacks, leading to poor quality of life and increased reintervention rate.6-8 We found the mean survival age was similar (19.1 vs. 18.6 years) among BIMA+R and SIMA+R±V groups might be due to the age at CABG surgery being identical and both belonging to multiple arterial graft CABG populations. In an RCT, Gaudino et al. observed that radial-artery grafts have a higher graft patency rate and a low adverse cardiac event over 5 years of follow-up, similar to our study results.9 The radial artery is believed to be disease-free with a good caliber and length, relatively resistant to the atherosclerosis process and has a good muscle layer facilitating better graft patency.7-9 In an international study coordinated in the United Kingdom, a randomized controlled trial pioneered by Taggart et al.10 evaluated the long-term mortality rate of bilateral versus single IMA grafts for CABG and observed no significant difference in all-cause of mortality over 10 years of follow-up, which is similar to other published articles.5-9 Moreover, Royse et al.,11 Rocha et al.,12 and Rayol et al.,13 observed better long-term survival benefits of multiple arterial CABG populations and encouraged the utilization of more arterial conduits, identical to the current study results. The preservation of graft patency is influenced by vascular endothelial nitric oxide (NO) and increased stress within the arterial circulation.14, 15 Nitric oxide helps maintain vascular tone, preventing platelet aggregation, white blood cell activation, thrombus formation, and smooth muscle cell proliferation. However, arterial conduits, particularly the radial artery, exhibit superior endothelium-dependent relaxation and remodeling under increased stress. In contrast, vein grafts show a decrease in the biological effects of NO and changes in gene expression, leading to vascular smooth muscle cell proliferation, acceleration of degenerative process, and atherosclerosis, which results in a poor graft patency rate.14, 15 According to the existing literature,8, 10, 13, 16 our revascularisation strategy was to achieve complete myocardial revascularisation utilizing more arterial conduits and arterial grafts (with sequential arterial grafts if needed) based on a distinct preoperative plan on angiographic findings to accomplish total arterial CABG. Further, existing articles6-8 found BIMA harvesting poses challenges for sternal wound infection; we found no significant long-term adverse outcome associated with utilizing BIMA grafts over 20 years. Insofar as we know, this is the most extensive TAR-CABG study in the United Kingdom; however, its nonrandomized retrospective observational methods put some methodological limitations despite enough statistical power. Although the study sample was male predominant and based on a heterogeneous group from a single institute, the survival rate in each study group and the odds ratio of potential risk factors are topics of high interest. Nevertheless, our redo-CABG cases were performed due to a new coronary lesion having study limitations as specific data on-site, territory, and percentage of the lesion is lacking. Furthermore, we believe an RCT or large observational study based on angiographic evaluation of graft patency rate following multi-arterial CABG will shed more light on this study outcome. Multiple arterial CABG surgery is feasible and has excellent long-term survival benefits compared to the mixed arterio-venous graft population over 20 years of follow-up. Aziz Momin: Conceptualization; formal analysis; methodology; resources; supervision; validation; visualization; writing—review and editing. Redoy Ranjan: Conceptualization; formal analysis; methodology; resources; validation; visualization; writing—original draft; writing—review and editing. Venkatachalam Chandrasekaran: Conceptualization; methodology; resources; supervision; validation; writing—review and editing. Redoy Ranjan is an Editorial Board member of Health Science Reports, and a coauthor of this article. To minimize bias, they were excluded from all editorial decision-making related to the acceptance of this article for publication. In accordance with the National Research Ethics Service, this retrospective study, using data already collated as patients received their usual care, did not require research ethics committee approval but adhered to international standards for GDPR (General Data Protection Regulation). The lead author Redoy Ranjan affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. The research data used to support the findings of this study are available from the corresponding author of this study upon request.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation S. V. Tresa Sangeetha, P. G. Kuppusamy, V. Chandrasekaran, A. Sangeerani Devi; Design implementation of smart fish pond monitoring system using IoT. AIP Conference Proceedings 30 January 2023; 2523 (1): 020015. https://doi.org/10.1063/5.0125610 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time. With the use of mobile devices, communication services generate numerous data for every moment. Given the increasing dense population of data, traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation. A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning (RKLSTM-CTMDSL) model is introduced for traffic prediction with superior accuracy and minimal time consumption. The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction. In this model, the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction. A large volume of spatial-temporal data are considered as an input-to-input layer. Thereafter, input data are transmitted to hidden layer 1, where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results. After obtaining the relevant attributes, the selected attributes are given to the next layer. Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns. Tversky similarity index outcomes are given to the output layer. Similarity value is used as basis to classify data as heavy network or normal traffic. Thus, cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model. Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.

We here present a case of a 54-year-old man with longstanding persistent atrial fibrillation refractory to direct current electrical cardioversion who underwent a concurrent convergent ablation and Atriclip exclusion of left atrial appendage. His preoperative echocardiography revealed dilated 5.8 cm left atrium with a normal left ventricular ejection fraction of 50%. Transmural isolation of pulmonary veins was performed through a subxiphoid approach, and 3 left-sided video-assisted thoracoscopic surgery ports were utilised to occlude the base of the left atrium appendage with the Atriclip device. A peri-operative transoesophageal echocardiogram confirmed left atrium appendage base occlusion, and the patient was in sinus rhythm after having a single 200 kJ direct current cardioversion shock. The postoperative period was uneventful, and the patient was discharged with preprocedural anticoagulant after 24 hours of the procedure and advised to come for follow up after 3 months.

Although the healthcare sector is generally “information-rich,” sadly, not all data are mined, which is necessary for finding hidden patterns and making wise decisions. In medical research and especially in the prediction of heart disease, sophisticated data mining methods are used. These methods are also used to predict the data that is contained in databases. In this article, prediction systems for heart failure have been examined using a larger number of input attributes. The method analyses 13 variables, including medical words like sex, blood pressure, and cholesterol, to forecast the risk that a patient will develop heart disease. 13 attributes have been used for prediction. In this study, smoking and obesity were introduced as two more characteristics. In the existing systems, we have used Naïve Bayes and neural networking systems, which have less accuracy compared to the proposed system algorithms. On a database of heart diseases, the proposed data mining classification algorithms J48 Decision Trees, Bagging, and Adaboost are examined. Based on the accuracy, these techniques' performances are contrasted. According to our findings, the Adaboost, J48 decision tree, and bagging accuracy are 92.1 percent, 91.62 percent, and 90.52 percent, respectively. Our investigation reveals that Adaboost, out of these three classification methods, accurately predicts heart failure.

Due to the increased growth of elderly people in recent years, healthcare systems face many challenges on the money spent for those people. Both quality and affordability has to be provided by the new technology which is the today’s need. When applying WSN technologies, the advantages such as continuous monitoring with alert mechanisms and relative information are to be satisfied. Among the other challenges, due to the deployed environment, security is a key challenge. As gateway connects to the wireless networks, it is the target area for many adversaries to launch various attacks. Initially, attacker launches node compromise attack which leads to node replication attack. The introduced security methods for intelligent healthcare monitoring system effectively detect replication attack and provide protection to the system. The potential application of proposed methods namely Exponential Moving Average based Replica Detection (EMABRD), Secured Ant Colony Optimization (SACOP) and Fingerprint based Zero Knowledge Authentication (FZKA) is applied to a real time environment. While comparing three algorithms, SACOP has higher detection probability of malicious nodes at the expense of increased storage and communication overheads over EMABRD and FZKA. FZKA performs better compared to EMABRD in terms of detection probability but at the cost of increased overheads. So, among the three algorithms, EMABRD is better in terms of overheads and SACOP is better in terms of detection probability.

In this paper, a new fractional differentiation-based active contour model for robust image segmentation is presented. A new edge energy is introduced, in which the contour evolution is driven by the difference between the fractional derivatives directed along the inward and outward normal directions of the evolving contour. We provide the level set formulation of this novel energy and show that this energy is minimized when there is an accurate alignment of the zeroth level set of the evolving contour with the actual object boundary. The proposed model outperforms other state-of-the-art active contour methods in eliciting weak/fuzzy boundaries in real world images and provides robust segmentation even under influence of various types of noise as quantified by segmentation metrics.

Copyright protection of digital images is an important commercial requirement to individual artists and large organisations alike. Wavelet-based image watermarking methods have been in practice due to their robustness against standard geometrical and image processing attacks. Convolutional Neural Networks (CNNs)-based watermarking methods are becoming popular as they provide a new dimension to the generation of a watermarked image, which is perceptually close to the original image when trained over a large class of images, thereby eliminating the need to train on each image that is to be watermarked. However, the watermark extraction performance of CNNs when used in standalone mode reduces in the presence of adversarial examples. In this study, we combine the robustness of a multi-level Discrete Wavelet Transform (DWT) and the power of CNNs and propose a robust blind grayscale image watermarking method. In the proposed method watermark is of the same size as the original image thereby demonstrating the robustness under increased payload as well. The quality of the extracted watermark is measured using Structural Similarity Index Measure (SSIM), Peak-Signal-to-Noise ratio (PSNR) and Normalized Cross Correlation (NCC). Our proposed method provides high quality watermark extraction under geometrical, image processing and adversarial attacks including second watermarking by an attacker.

Crop damage is one of the core and perennial problems in agricultural field. Most of the researchers in both academic institutions/universities, government organizations and farmers were concentrating on finding optimized solutions to overcome crop damage occurring due to natural threats. Among many natural threats, crop damage is mainly induced by birds, particularly peacocks in the southern regions. Crops are also affected due to the seasonal variations and in different stages of crop growth. As the national bird of India is Indian peafowl (Pavo cristatus), it must be protected in spite of it causes various threats to the farmland owners. Peacock damages crop in the cultivated farmlands by migrating from forest into semi-rural and residential areas after monkeys. The peacocks only damage crops in the fields, but the monkeys scare the humans by getting closer. A decade back, people (including farmers) were surprised and enjoyed seeing peacock in their place. Now farmers were frightened because it invades their farmlands by damaging their crops, thereby causing severe economic loss to the farmers. In northern region of Tamil Nadu, especially Erode and Coimbatore districts, nearly 65% of the people are directly or indirectly dependant on agricultural sector for economic survival. The paper focuses on helping the farmers to protect the crop damage from peacock. Mostly, in our surrounding, the crops are frequently damaged by peacocks. The farmers used to keep away the peacock from the farmland by making sounds by themselves. But that is ineffective in repelling peacock mainly in large fields. So, peacock repellent technique is proposed to solve the above problem. The paper unveils the importance of using interdisciplinary approach to develop an eco-friendly technique to reduce crop damage without affecting peacock.

The use of mobile devices in the medical and healthcare world has been a determining factor. The whole region has a mobile health sticker (mHealth). For mHealth, it is important to develop and use mobile applications. In turn, mHealth applications have innumerable targets and objectives. As a result, in the app stores, mHealth apps can be found in MediBuddy names, doctors on demand etc. While these applications are readily accessible in the google play store, this creative application’s primary aim is lacking, which is the accuracy of medical details. One does not communicate to the doc who is, on the other hand, exactly with his body temperature or pulse rate or pressure level. Therefore, we propose a prototype in this article that provides the mobile application with the necessary information through serial communication using the Bluetooth, which is sufficient for the doctor to prescribe medicines.

Construction of cloud computing and promotion of applications such as social network service and smart city have driven the need for trust mechanism with the rapid developments of Internet of things (IoT). In the existing methods, storing the trust value incurs high storage overhead leading to energy inefficiency and reliability of identifying trustful or untrustworthy node is very less. In order to avoid these drawbacks, Secured Ant Colony Optimization (SACOP) based on trust sensing model is proposed to detect the node replication attack. Firstly, node’s trust value is estimated using direct and indirect trust evaluation model to identify the malicious node in the clustered network. Secondly, ant colony routing algorithm is introduced to select the secured optimal path using probability to select the next hop node for data forwarding. As the probability is calculated using the residual energy, trust and pheromone values, energy expenditure among all nodes gets balanced. The proposed algorithm performs better in terms of packet loss rate, time delay, throughput and average energy consumption compared to existing scheme DDR.

Super-resolution (SR) is the method of obtaining high-resolution (HR) images or image sequences from one or more low-resolution (LR) images of a scene. Huang et al. in 2015 proposed a transformed self-exemplar internal database technique which takes advantage of fractal nature in an image by expanding patch search space using geometric variations. This method fails if there is no patch redundancy within and across image scales and also if there is a failure in detecting vanishing points (VP) which are used to determine perspective transformation between LR image and its sub-sampled form. In this paper, we expand the patch search space by taking advantage of temporal dimension of image frames in the scene video and also use an efficient vanishing point (VP) detection technique by Lezama et al. in 2014 and are thereby able to successfully super-resolve even the failure cases of Huang et al. and an overall improvement in PSNR. We also focused on reducing the computation time by exploiting the embarrassingly parallel nature of the algorithm. We achieved a speedup of six on multi-core, up to 11 on GPU, around 16 on hybrid platform of multi-core and GPU by parallelising the proposed algorithm. Using our hybrid implementation, we achieved 32x super-resolution factor in limited time.