An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm

Image segmentation is a significant task for image analysis which is at the middle layer of image engineering. The purpose of segmentation is to decompose the image into parts that are meaningful with respect to a particular application. The proposed system is to boost the morphological watershed method for degraded images. Proposed algorithm is based on merging morphological watershed result with enhanced edge detection result obtain on pre processing of degraded images. As a post processing step, to each of the segmented regions obtained, color histogram algorithm is applied, enhancing the overall performance of the watershed algorithm. Keywords – Segmentation, watershed, color histogra

Primary chiasmal sarcoid granuloma masquerading as glioma of the optic chiasm

A 37-year-old woman presented with a 6 months history of headaches and memory impairment. Examination showed no neurological deficit with normal vision. MRI scans showed an enlarged optic chiasm. There was no dural or leptomeningeal enhancement or hydrocephalus. Open biopsy of the suprasellar mass showed non-caseating chronic granulomatous inflammation compatible with sarcoidosis. Systemic features of sarcoid were absent. Patient showed marked improvement on steroid therapy

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges

A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempts to discuss the key components and issues involved in transforming conventional cities into smart cities with a special focus on cyber-physical systems in the Indian context. The paper primarily focuses on the infrastructural facilities and technical knowhow to smartly convert classical cities that were built haphazardly due to overpopulation and ill planning into smart cities. It further discusses cyber-physical systems as a core component of smart city setups, highlighting the related security issues. The opportunities for businesses, governments, inhabitants, and other stakeholders in a smart city ecosystem in the Indian context are also discussed. Finally, it highlights the issues and challenges concerning technical, financial, and other social and infrastructural bottlenecks in the way of realizing smart city concepts along with future research directions

Exploring the Digital Support Needs of Caregivers of People With Serious Mental Illness

In low-and middle-income countries like India, people with severe mental illness (PSMI) rely on their families as a primary source of care, given the lack of support from healthcare systems. The demanding nature of caregiving places significant physical and mental demands on caregivers, who are the primary source of support to PSMI. We explore how caregivers in under-resourced settings can be better supported through everyday digital technologies. We conducted interviews with caregivers (from urban and rural India), as well as workshops with professionals from Indian NGOs that work directly with PSMIs. We found that technology has the potential to (1) provide carer-centred support that empowers carers who experience stigma and issues with existing support networks; (2) provide support for carers to overcome barriers and progress in the recovery of the PSMI. We conclude with design considerations, proposing how an online peer community can leverage carers’ expertise to actualise support provision

Development and Experimental Study of Bond between FRP Rebar and Concrete

Fiber reinforced polymer (FRP) bars are frequently used in civil engineering. due to their many benefits, which include excellent weight-to-strength ratio, light weight, ease of handling, electromagnetic neutrality, and lack of rust, as an alternative to reinforcement steel. FRP has also developed into a competitive and cost-effective structural material as production machinery advances and more industries become industrialized. In this study, the flexural presentation of concrete reinforced (RC) beams and fiber-reinforced polymer (FRP) bars after conditioning for 6, 9, and 12 months with simulated saltwater in a wet-dry environment cycling is investigated. This study's goal is to present new developments in the study of FRP-reinforced concrete structures based on current research. Among the subjects covered in this study are the bond presentation of FRP bars' flexural behavior, concrete's compression behavior, and concrete of ductility structures reinforced with FRP bars in recent years all over the world. The two types of FRP bars employed are basalt FRP (BFRP) and steel-FRP composite bars (SFCBs). Steel bars are used as a point of reference. The beams are subjected to a continuous load during conditioning. There are 24 simple-supported rays in all that have been verified

Machine Learning Approaches for Fake User and Spammer Detection: A Comprehensive Review and Future Perspectives

The rise of digital platforms has given way to a surge in fraudulent activities, including the creation of fake user accounts and the prevalence of spammers. These malevolent actions present significant challenges to the security and integrity of these platforms, necessitating effective detection and prevention measures. This paper offers an extensive review of machine learning (ML) techniques currently employed for fake user and spammer detection. The paper explores a range of traditional ML algorithms such as decision trees, support vector machines, and logistic regression, as well as more complex deep learning models like convolutional neural networks (CNN) and recurrent neural networks (RNN). It also examines unsupervised and semi-supervised learning strategies that can be used when labeled data is scarce. Furthermore, we discuss the key challenges in detecting fake users and spammers, including the dynamic nature of spamming tactics, evolving deceptive strategies, data imbalance, and privacy issues. We propose potential solutions to these challenges like transfer learning, active learning, federated learning, and privacy-preserving ML techniques. The paper concludes with an exploration of emerging technologies such as explainable AI and reinforcement learning and their potential to enhance detection system performance and interpretability. It also provides insights into promising future research directions in this critical area

Enhancing Spammer Fake Profile Detection on Social Media Platforms using Artificial Neural Networks

The proliferation of social media platforms has led to an increase in spammer fake profiles, posing significant security, privacy, and trustworthiness concerns. Traditional manual monitoring and content filtering techniques are insufficient to combat this growing issue, necessitating the development of more efficient and accurate detection methods. Machine learning techniques have been increasingly employed for this purpose, demonstrating promising results in identifying spammers and fake profiles. This paper presents a novel approach for spammer fake profile detection using Artificial Neural Networks (ANNs) to enhance the accuracy of the detection process. Our proposed ANN-based method addresses the challenges associated with spammer fake profile detection, such as the dynamic nature of spammers, data heterogeneity, scalability, and imbalanced datasets. We evaluate the performance of our method on real-world datasets and compare it with existing machine learning techniques, demonstrating its effectiveness and superiority in detecting spammers and fake profiles with higher accuracy. This research contributes to ongoing efforts to secure social media platforms, ensuring the trustworthiness of online content and providing a safer user experience

Feature Extraction and Classification of EEG Signals Using Neural Network Based Techniques

EEG stands for Electroencephalogram. EEG is used to record signals from brain; signals are recorded from the scalp or cortex of brain. EEG used for both clinically purpose as well as for scientifically purpose. Hence measurement of EEG signals plays an important role in mind/brain studies. Reorganization of EEG signals from brain is one of the most overriding approaches to extract the data/knowledge from mind/brain dynamics. Analyzing Electrical activity of brain through EEG provide medical science to examine different brain diseases. Electrical activity of brain can easily be classified as normal brain waves or abnormal brain waves. Normal brain waves used to study various states of mind where as abnormal brain waves used to indicate medical problems. Classification of EEG signals play important role in medical science, some important applications for EEG wave classification are diagnosis of sleep disorders and construction of BCI to assist disabled person.  Reorganization of EEG signals from brain is one of the most overriding approaches to extract the data/knowledge from mind/brain dynamics. Analyzing Electrical activity of brain through EEG provide medical science to examine different brain diseases

What families want - an assessment of family expectations in the ICU.

Introduction: Families of Patients admitted in the intensive care units (ICUs) experience high levels of emotional stress. Access to information about Patient\u27s medical conditions and quality relationships with healthcare staff are high priority needs for these families and meeting these needs of the family members is a primary responsibility of ICU physicians and nurses. Methodology: Our objectives were to assess the expectations of ICU Patients\u27 families that can be fulfilled by physicians and nurses. The design was a descriptive, exploratory questionnaire based study over 6 months in the multidisciplinary ICU of a tertiary care hospital. Results: Of 205 interviews, the median age of the Patient was 28 years. One hundred and nineteen (58%) were male and Eighty six (42%) Patients were female. 163 (79.5%) of the relatives were Next of kin, and 133 (64.9%) were male members. Of the family members, 20 (9.8%) were spouses. One hundred and forty two (69.3%) belonged to Middle income group. Ninety nine (48.3%) were Graduates of high school or above. Relation to Patient, sex of relative, DNR status of Patient and age of relative were statistically significant to make a difference to the satisfaction score. The majority of the relatives reached a score of 22-25. Conclusion: We conclude that families of critically ill Patients were generally satisfied with communication in the ICU, however, our limitations are the cohort in our urban based tertiary care hospital may not adequately represent the majority of our population which is poor and illiterate and many other factors such as misunderstanding of medical knowledge and a more patriarchal attitude of physicians may affect family needs and satisfaction scores