Search results for: bliss

Authors: Myriam Rudaz, Thomas Ledermann, Frank D. Fincham

Abstract:

We provide initial findings on the development and validation of the Genuine Happiness Scale (GHS). Based on the Buddhist view of happiness, genuine happiness can be described as an unlimited, everlasting inner joy and peace that gives a person the inner resources to deal with whatever comes his or her way in life. The sample consisted of 678 young adults, with 432 adults participating twice, approximately six weeks apart. Exploratory and confirmatory factor analysis supported a unidimensional factor structure of the GHS. Hierarchical regression analysis revealed that caring for bliss, mindfulness, and compassion predicted genuine happiness longitudinally above and beyond genuine happiness at baseline. We discuss the usefulness of the GHS as an outcome measure for evaluating mindfulness- and compassion-based intervention programs.

Keywords: happiness, bliss, well-being, caring for bliss, mindfulness, compassion

Procedia PDF Downloads 86

Authors: Shyam Sunder Gupta

Abstract:

GOD is an infinite source of knowledge. From time to time, as per the need of mankind, GOD keeps revealing, some small, selected part of HIS knowledge as WORDS, to a chosen entity whose responsibility is to function as Messenger and share WORDS, in the form of verses, with common masses. GOD has confirmed that Messenger may not understand every WORD revealed to him, and HE directs Messenger to learn from persons who have knowledge of WORDS revealed in earlier times, as some revealed content is identical and some different by design. In due course of time, Verses, as communicated orally, are collected, and edited by an individual in a planned manner or by a group of individuals and get edited unintentionally and converted in the form of Scripture. Whatever gets collected, depending on the knowledge of the Editor(s), some errors, scientific and other forms, get into Scripture. In the present world, there are three major religions: Christianity, Islam and Hinduism, accounting for more than two-thirds of the world’s population. Each of the religions has its own Scripture, namely the Bible, Quran, and Veda. Since the source of WORDS for each of these Scriptures is the same, there is ONENESS of all Scriptures. There are amazing similarities between the events described, like the flood during the time of Noah and King Satyavara. The description of the creation of man and woman is identical. Description of Last Day, categorization of human beings, identical names, etc., have remarkable similarities. Ram, the hero of Ramayana, is a common name in Hinduism and two of Jesus’ ancestors’ names were Ram and many names in the Bible are derived from Ram. Attributes of GOD are common in all Scriptures, namely, GOD is Eternal, Unborn, Immortal, Creator of Universe(s) and everything that exists within the Universe, Omnipotent, Omnipresent, Omniscient, Subtlest of all, Unchangeable, Unique, Always Works, Source of Eternal Bliss, etc. There is the Oneness of GOD.

Keywords: GOD, scriptures, oneness, WORDS, Jesus, Ram

Procedia PDF Downloads 32

Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 49

Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

Procedia PDF Downloads 53