HealthBlock: A Blockchain-IoT Fusion for Secure Healthcare Data Exchange

Managing healthcare data while ensuring its security and privacy is critical to providing quality care to patients. However, traditional approaches to healthcare data sharing have limitations, including the risk of data breaches and the lack of privacy-preserving mechanisms. This research paper proposes a novel hybrid blockchain-IoT approach for privacy-preserving healthcare data sharing that addresses these challenges. Our system incorporates a private blockchain for protected and tamper-proof data sharing, with privacy-preserving techniques such as differential privacy and homomorphic encryption to protect patient data. IoT devices are utilized to collect and transmit real-time data, equipped with privacy-preserving mechanisms such as data anonymization and secure transmission protocols. Our approach achieved an accuracy rate of 98% for access control and a 99.6% success rate for data privacy protection. Furthermore, our proposed system demonstrated improved data storage and retrieval performance, with a data storage overhead reduction of up to 86% and a data retrieval time reduction of up to 81%. These results indicate the potential of our approach to enhance the security, privacy, and efficiency of healthcare data management, contributing to improved patient care outcomes

A MODIFIED DEEP CONVOLUTIONAL NETWORK FOR DETECTION OF COVID19 FROM CHEST X-RAYS BASED ON CONCATENATION OF IMAGE PREPROCESSING TECHNIQUES AND RESnCOV

The fast-spreading coronavirus disease called COVID-19 has impacted millions of people worldwide. It becomes difficult for medical experts to rapidly detect the illness and stop its spread because of its rapid growth and rising numbers. One of the newer areas of study where this issue can be more carefully addressed is medical image analysis. In this study, we implemented an image processing system utilizing deep learning and neural networks to previse the 2019-nCoV using chest roentgen ray images. In order to recognize COVID-19 positive and healthy patients using chest roentgen ray images, this paper suggests employing convolutional neural networks, deep learning, and machine learning. We proposed a neural network composed of various features taken from two convolutional neural networks, ResNet50 and ResNet152V2, in order to successfully manage the intricate structural complexity of an image. We tested our network on 7940 images to see how well it performs in real-world situations. The proposed network detects normal and COVID-19 cases with an average accuracy of 95% and can be used as an aid in the radiology departmen

Deep Convolutional Neural Network with Image Processing Techniques and Resnet252v2 for Detection of Covid19 from X-Ray Images

The 2019 coronavirus disease, also known as SARS-CoV-2, has emerged as a highly contagious viral infection with a significant global impact. It has rapidly spread across various regions, resulting in a substantial number of individuals being affected by this disease. Research findings indicate that the rapid and widespread transmission of the disease has posed significant challenges for healthcare professionals in promptly diagnosing the condition and implementing effective measures to contain its propagation. The automation of the diagnostic procedure has emerged as a critical necessity. According to research findings, the implementation of this particular measure has been shown to significantly enhance work efficiency while simultaneously safeguarding healthcare workers from potential exposure to harmful viruses. Medical image analysis is a rapidly growing area of research that offers a promising solution to address this problem with greater precision. This research paper introduces a novel approach for predicting SARS-CoV-2 infection using chest radiography images..

Ethnomedicinal Plants Survey and Documentation Related to Paliyar Community

The present study is mainly focused on the ethnomedico botany of the tribe Paliyars a dominant ethnic group inhabiting the Western Ghats (Off Shoots) of Sirumalai Hills, Dindigul district. Tamil Nadu. In the present study the focus is on survey, documentation and enumeration of the medicinal plants practiced by the tribal Paliyars, As an outcome of the present investigations 30 plants have been identified and documented. The life style of the Paliyar was also studied

Effectiveness of Capacity Building Programme on Knowledge and Practice of Amnicot among Staff Nurses

STATEMENT OF THE PROBLEM : A Pre Experimental Study to Assess the Effectiveness of Capacity Building Programme on Knowledge and Practice of Amnicot among Staff Nurses at Selected Hospital Chennai. OBJECTIVES OF THE STUDY : 1. To assess the level of knowledge and practice of amnicot among staff nurses in the selected hospital. 2. To assess the effectiveness of the capacity building programme for staff nurses on amnicot by comparing pretest and posttest knowledge and practice. 3. To assess the level of satisfaction on the capacity building programme for staff nurses on knowledge and practice of amnicot. 4. To find out the association between the selected variables and pretest and posttest knowledge and practice of capacity building programme of amnicot among staff nurses. The conceptual framework of the study was developed on the basis of Ottawa Adaptation Theory. The variables of the study were knowledge and practice of staff nurses on amnicot as dependent variables and capacity building programme of amnicot as independent variables, were formulated. An extensive review of literature and guidance by experts formed the foundation to the development of structured knowledge questionnaires and intervention on capacity building programme. A pre experimental one group pretest and posttest research design was used to achieve the objectives of the study. The present study was conducted in the Apollo Cradle Hospital, Chennai, with the sample size of 30 staff nurses, selected through purposive sampling technique and written consent was obtained from the staff nurses. Pretest assessment was done with predetermined tools. Knowledge and practice were assessed using structured questionnaire and an observational check list respectively. The intervention of capacity building programme of amnicot was carried out for 30 staff nurses. It was done by using lecturer cum discussion and demonstration method for 2 hours which was followed by redemonstration. Posttest assessment was done with the same questionnaire and a checklist for the staff nurses at the interval of 1 week after intervention. Then the level of satisfaction of staff nurses was assessed using rating scale. The data obtained were analyzed using Descriptive and Inferential statistics. MAJOR FINDINGS OF THE STUDY : • Majority of the staff nurses were below 25 years (93.33%) and all of them were Females (100%). Their designation was staff nurse (100 %). Most of the staff nurses had less than 1 year of work experience (66.67%) in labour room and have not received previous information regarding amnicot (66.67%). • Most of the staff nurses had moderate knowledge (60%) and (23.33%) had inadequate knowledge about amnicot. After intervention majority of the staff nurses had adequate knowledge (63.33%) and others (20%) had moderate knowledge. • Mean and standard deviation of posttest knowledge scores was high (M=20.3, SD=3.81) when compared to the pretest (M= 12.46, SD=4.08). The differences were found statically significant at p< 0.001. This can be attributed to the effectiveness of the knowledge and practice of amnicot among staff nurses. Hence the null hypothesis Ho1 “There will be no significant difference between Pretest and Posttest level of Knowledge and Practice of amnicot among staff nurses” was rejected. • In pretest 23.33% of their practice was good and16.66% had very good practice whereas in posttest 43.33% of their practice was good followed by excellent practice (30%) and very good practice (20%). • Mean and standard deviation of posttest practice scores were high (M=14.13, SD=4.32) when compared to the pretest practice scores (M = 8.6, SD= 5.03). The differences were found to be statically significant at p< 0.001. Hence the null hypothesis Ho1 “There will be no significant difference between pre and post test level of knowledge and practice of amnicot among staff nurses” was rejected. • Majority of the staff nurses (93.33%) were highly satisfied with the researcher, (90%) on the capacity building programme and (93.33%) on effectiveness of amnicot. • The study finding revealed that, there was no significant association between the selected background characteristics with the pre test and post level of knowledge on amnicot among staff nurses. Hence the null hypothesis Ho2 “There will be no significant association between the selected variables and level of knowledge in pretest and posttest among staff nurses” was retained and. • The study finding revealed that, there was no significant association between the selected background characteristics with the pre test and post level of practice of amnicot among staff nurses. Hence the null hypothesis HO3 “There will be no significant association between the selected variables and level of practice in pretest and posttest among staff nurses” was retained. RECOMMENDATIONS : • The study can be conducted with larger samples to generalize the results. • The study can be replicated in different settings. • Same study can be conducted among nursing student. • A comparative study can be conducted between private and government settings

Poboljšanje slike i vrednovanje radnih značajki korištenjem raznih filtara na daljinski mjerene podatke IRS-P6 satelita Liss IV

This paper presents fast and effective filtering techniques for image enhancement from remote sensing Indian remote sensing satellite P6 Liss IV remotely sensed data like Near-Infrared band. There are four filtering techniques used for image enhancement based on spatial domain filters and frequency domain filters such as median filter, wiener filter, bilateral filter and Gaussian homomorphic filter and selected noises salt and pepper and Gaussian noise used with filter. Selected images tested with each filter and based on PSNR performance metric value and best filtering technique identified from these filters. Finally, Gaussian homomorphic filtering technique is suitable for image enhancement of the Liss IV remotely sensed Near-Infrared band. Image enhancement technique is preprocessing for future work such as edge detection and image segmentation.U radu su prikazane brze i učinkovite tehnike filtriranja za poboljšanje slike iz podataka u bliskom infracrvenom području dobivenih indijskim satelitom za daljinska istraživanja P6 Liss IV. Korištene su četiri tehnike filtriranja temeljene na filtrima u prostornoj i frekvencijskoj domeni kao što su: medijan filtar, Wiener filtar, bilateralni filtar i gaussovski homomorfni filtar uz odabrane šumove “salt and pepper” i gaussovski šum s filtrom. Odabrane slike testirane su sa svakim od filtera te je na temelju metričke vrijednosti PSNR (Peak Signal Noise Ratio) radne značajke prepoznata najbolja tehnika filtriranja. Konačno se pokazalo da je gaussovska homomorfna tehnika filtriranja prikladna za poboljšanje slika dobivenih pomoću satelita Liss IV u bliskom infracrvenom području. Tehnika poboljšanja slike je predobrada za budući rad, kao što je detekcija ruba i segmentacija slike

Probiotic Effect of Lactobacillus Isolates Against Bacterial Pathogens in Fresh Water Fish

A total of 59 Lactobacillus isolates were isolated from 5 different fresh water fish such as Cat fish ( Clarias orientalis) , Hari fish (Anguilla sp), Rohu fish ( Labeo rohita), Jillabe fish (Oreochromis sp) and Gende fish ( Punitus carnaticus). Among the 59 isolates only 4 Lactobacillus isolates were selected for further study. Based on morphological and biochemical characteristics, the isolates were identified as Lactobacillus sp. The pathogen were isolated from infected cat fishes, characterized and identified as Vibrio parahaemolyticus, Aeromonas sp and Aeromonas salmonicida. The Lactobacillus isolates were screened for antagonistic activity against Aeromonas, Vibrio sp. by agar diffusion assay. Among the 4 isolates, Lactobacilli RLD2 showed significant antagonistic activity against Aeromonas and Vibrio sp alone. and was further evaluated by standard plate count assay for the viability of pathogen. The isolate was multiplied and the fish feed was supplement with Lactobacillus isolates. The results reveal that the size, weight of the fish was statically increased in comparison to that of control fish. The present study concluded that the Lactobacillus isolates could be used as probiotic bacteria in aquaculture, to manage aeromonasis

A Jamming Attacks Detection Approach Based on CNN based Quantum Leap Method for Wireless Sensor Network

The wireless sensor network is the most significant largest communication device. WSN has been interfacing with various wireless applications. Because the wireless application needs faster communication and less interruption, the main problem of jamming attacks on wireless networks is that jamming attack detection using various machine learning methods has been used. The reasons for jamming detection may be user behaviour-based and network traffic and energy consumption. The previous machine learning system could not present the jamming attack detection accuracy because the feature selection model of Chi-Squared didn’t perform well for jamming attack detections which determined takes a large dataset to be classified to find the high accuracy for jamming attack detection. To resolve this problem, propose a CNN-based quantum leap method that detects high accuracy for jamming attack detections the WSN-DS dataset collected by the Kaggle repository. Pre-processing using the Z-score Normalization technique will be applied, performing data deviations and assessments from the dataset, and collecting data and checking or evaluating data. Fisher’s Score is used to select the optimal feature of a jamming attack. Finally, the proposed CNN-based quantum leap is used to classify the jamming attacks. The CNN-based quantum leap simulation shows the output for jamming attacks with high precision, high detection, and low false alarm detection

Impact on health and provision of healthcare services during the COVID-19 lockdown in India: A multicentre cross-sectional study

Introduction The COVID-19 pandemic resulted in a national lockdown in India from midnight on 25 March 2020, with conditional relaxation by phases and zones from 20 April. We evaluated the impact of the lockdown in terms of healthcare provisions, physical health, mental health and social well-being within a multicentre cross-sectional study in India. Methods The SMART India study is an ongoing house-to-house survey conducted across 20 regions including 11 states and 1 union territory in India to study diabetes and its complications in the community. During the lockdown, we developed an online questionnaire and delivered it in English and seven popular Indian languages (Hindi, Tamil, Marathi, Telegu, Kannada, Bengali, Malayalam) to random samples of SMART-India participants in two rounds from 5 May 2020 to 24 May 2020. We used multivariable logistic regression to evaluate the overall impact on health and healthcare provision in phases 3 and 4 of lockdown in red and non-red zones and their interactions. Results A total of 2003 participants completed this multicentre survey. The bivariate relationships between the outcomes and lockdown showed significant negative associations. In the multivariable analyses, the interactions between the red zones and lockdown showed that all five dimensions of healthcare provision were negatively affected (non-affordability: OR 1.917 (95% CI 1.126 to 3.264), non-accessibility: OR 2.458 (95% CI 1.549 to 3.902), inadequacy: OR 3.015 (95% CI 1.616 to 5.625), inappropriateness: OR 2.225 (95% CI 1.200 to 4.126) and discontinuity of care: OR 6.756 (95% CI 3.79 to 12.042)) and associated depression and social loneliness. Conclusion The impact of COVID-19 pandemic and lockdown on health and healthcare was negative. The exaggeration of income inequality during lockdown can be expected to extend the negative impacts beyond the lockdown

A novel strategy for waste prediction using machine learning algorithm with IoT based intelligent waste management system

Internet of Things (IoT) has now become an embryonic technology to elevate the whole sphere into canny cities. Hasty enlargement of smart cities and industries leads to the proliferation of waste generation. Waste can be pigeon-holed as materials-based waste, hazard potential based waste, and origin-based waste. These waste categories must be coped thoroughly to make certain of the ecological finest run-throughs irrespective of the origin or hazard potential or content. Waste management should be incorporated into ecological preparation since it is a grave piece of natural cleanliness. The most important goalmouth of waste management is to maintain the pecuniary growth and snootier excellence of life by plummeting and exterminating adversative repercussions of waste materials on environment and human health. Disposing of unused things is a significant issue, and this ought to be done in the best manner by deflecting waste development and keeping hold of cost, and it involves countless human resources to deal with the waste. These current techniques predominantly focus on cost-effective monitoring of waste management, and results are not imprecise, so that it could not be developed in real time or practically applications such as in educational organizations, hospitals, and smart cities. Internet of things-based waste management system provides a real-time monitoring system for collecting the garbage waste, and it does not control the dispersion of overspill and blowout gases with poor odor. Consequently, it leads to the emission of radiation and toxic gases and affects the environment and social well-being and induces global warming. Motivated by these points, in this research work, we proposed an automatic method to achieve an effective and intelligent waste management system using Internet of things by predicting the possibility of waste things. The wastage capacity, gas level, and metal level can be monitored continuously using IoT based dustbins, which can be placed everywhere in city. Then, our proposed method can be tested by machine learning classification techniques such as linear regression, logistic regression, support vector machine, decision tree, and random forest algorithm. The proposed method is investigated with machine learning classification techniques in terms of accuracy and time analysis. Random forest algorithm gives the accuracy of 92.15% and time consumption of 0.2 milli seconds. From this analysis, our proposed method with random forest algorithm is significantly better compared to other classification techniques