This paper critically analyzed Abraham Maslow's well-known hierarchy of needs theory (1943), identified its shortcomings, and examined previous studies in the literature on qualitative research methods. Moreover, it discussed the suitability of the theory to human needs and its scientific nature, considering the qualifications, dynamics, variability, and nature of human needs. Maslow proposed a rigid hierarchical framework, categorizing human needs from physiological to self-actualization. Over time, the theory faced criticism for disregarding scientific principles and overlooking the objective and subjective aspects (...) of human needs. Instead of rigorous scientific research, the theory heavily relied on Maslow's personal observations and clinical experiences. This paper posits that Maslow overlooked socio-cultural influences that shape motivation and emphasizes how social and economic contexts prioritize needs. Moreover, the lack of empirical evidence is identified as a weakness compromising the theory's validity. The article also, proposes an updated framework considering the objectivity and subjectivity of human needs, scientific research principles, technological connections, sustainability, socio-cultural influences, arbitrary human behavior and needs, personal preferences, and emotional factors. It advocates for acknowledging that needs circulate dynamically through interpersonal networks rather than being staged rigidly, thereby supporting theoretical evolution. Additionally, the paper identifies and offers potential solutions to eight fundamental issues associated with the theory. In this study, a literature review was conducted, and Maslow's hierarchy of needs was analyzed and its deficiencies were identified and eliminated. Igor Kospov's model was examined and explained to eliminate its flaws. Finally, instead of Maslow's hierarchy of needs, the article proposes a model or set of clustered needs that are compatible with human needs and scientific, dynamic, interactive, prioritized by humans, and influenced and influenced by culture, technological developments and the human environment. The model, or clustered set of needs, consists of several clusters. These clusters are interconnected and influence each other. In other words, the same person can take steps towards self-actualization and experience the indispensable feature of social belonging while he satisfies the physiological needs of drinking water and eating, because human needs can vary from person to person, from place to place, from time to time and from situation to situation, even within the same person. (shrink)

Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. *e proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the (...) other algorithms. *e experimental results visualize the data using the boxplot for better understanding of attribute distribution. *e performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. *e comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

The bruxism is a medical sleep syndrome it is the remedial span for crushing the tines and gritting the jowl. Human rarely chore their tines and jowl, slightly than crushing their teeth lacking it producing any signals. The symptoms of bruxism are arduousness in the jowl joint, breakable teeth, headache, earache and difficulty in open in mouth etc. The causes of bruxism are snooze sickness, pressure and nervousness. The REM is a rapid eye movement it's a stages of sleep. The (...) EEG signal are used in the measurement of neuron, the alpha, beta, gamma, theta and delta wave are used in the prognostic of bruxism syndrome. It's used in MATLAB coding by the six steps in prognostic in bruxism. Md Belal Bin Heyat | Faijan Akhtar | Shadab Azad "Comparative Analysis of Original Wave & Filtered Wave of EEG signal Used in the Prognostic of Bruxism medical Sleep syndrome" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1 , December 2016,. (shrink)

Health Level 7 (HL7) is an international standards development organisation in the domain of healthcare information technology. Initially the mission of HL7 was to enable data exchange via the creation of syntactic standards which supported point-to-point messaging. Currently HL7 sees its mission as one of creating standards for semantic interoperability in healthcare IT on the basis of its flagship “version 3” (v3). Unfortunately, v3 has been plagued by quality and consistency issues, and it has not been able to keep pace (...) with recent developments either in semantics and ontology or in computer science and engineering. HL7’s response has been to develop its “Services-Aware Interoperability Framework” (SAIF), which is intended to provide a foundation for work on all aspects of standardization in HL7 henceforth. We here summarise the major design principles that must be satisfied by a semantic interoperability framework – principles relating both to static semantics and to computational behaviour. We then assess the SAIF in light of these principles. We conclude that the SAIF is not in a position to support the needed reform of the HL7 v3 family of standards. (shrink)