Volume 12 | Issue 1 |

 Abstract

This survey study uses a variety of machine learning and deep learning algorithms across several utilization domains to investigate the heterogeneous field of facial emotion identification. The review covers four different research papers, each of which adds special methods and insights to the developing subject of Recognizing Emotions. Various contexts are analyzed: online learning environments, deep learning- based emotion detection, generic facial emotion identification, and real-world subject-based emotional state recognition. This study offers a thorough review of the state-of-the-art in recognizing facial expressions using through a comparative analysis of the approaches, datasets, and attained accuracies.

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 Keywords

Deep Learning, Support Vector Machine, Convolutional Neural Network, Artificial Neural Network, Inception-V3, VGG19, ResNet-50, k-NN, MLP, Emotion Detection, Online Learning, Real-time Engagement, Survey Paper.

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A fundamental feature of education in modern democratic societies is the emphasis laid on the equalization of educational opportunists (henceforth, EEO). But the ideal of EEO as held by any society has always been the subject to change and interpretations. Successive interpretations of EEO are more demanding with respect to the changes they require in the current state of education. Initially the concept of EEO was limited only to the absence of formal barriers, later it was realized that educational opportunities cannot be solely construed in terms of the formal features of educational institutions, instead they must be construed in terms of the interaction between these features and psycho-social traits of students which are shaped by their family and the social groups they belong to. From 1960 onwards most of researches. particularly in field of sociology of education shifted their focus on the impact of psychosocial traits that has on education, and how these traits are influenced by social structures. In this backdrop this study has been conceived as it attempts to explore the intersectional impact of social class, caste and gender on students' educational expectations and attainment.

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Intersectional Impact of Social Class, Caste and Gender on Student's Educational Expectations and Attainment

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Sentiment analysis, originating in the early 2000s, has evolved from rule-based methods to advanced machine learning models. Widely applied across industries, it deciphers opinions in textual data. Despite advancements, challenges persist, including handling sarcasm, context nuances, and cultural variations, prompting ongoing research to enhance its accuracy and versatility. This paper delves into the intricate realm of sentiment analysis, providing a comprehensive exploration of its fundamental principles and techniques. The study begins by elucidating the basics of sentiment analysis, unraveling the core concepts that form the foundation of this field. The paper scrutinizes how sentiment analysis has transcended the binary classification of positive and negative sentiments, venturing into nuanced analyses that capture the complexity of human emotions expressed in text. In addition to comprehensively covering the existing landscape, this paper engages in foresight, contemplating the future frontiers of sentiment analysis. We ponder the potential applications and advancements that may emerge as technology progresses, envisioning the role sentiment analysis might play in diverse industries.

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 Keywords

Opinion Mining, CNN, RNN, LSTM, Deep Learning, Neural Networks

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 Abstract

Transgender in the Tamil community one among the recorded thus in the Tamil literature is the fact about the world or life of Transgender who are neither male nor female make and as such called third-genders. In such a Tamil literature, we can also find facts about Transgender descriptions how these Transgender are being forced and engaged themselves willfully. We can also find facts about homosexual and lesbian acts done by both males and females in such a Tamil literature. About these people above said there are numerous concepts are being put forward from various quarters. In this context it is all important to know that our ancient Tamils were pondering seriously about these concepts ever since the long ages past. This is the main subject matter this research centering on and thus explains the title. Aravanis or Thirunangaigal are now called Transgender. These people who are neither males nor females are named as Ali, Pedi, Pedu, Kosa, Nabunjagan, Annagan and such like in the ancient Tamil literatures Keeravadai, vonbadhu, Potta, Pottaiyan, and Pombala Satti are some names the today's colloquial language calls them by. The research we are undertaking explains the differences between these two categories quite explicitly indeed. Clause 377 of the Indian penal code provides that, "If any one enjoys intercourse of his own volition and in a loved manner with either one of men or women or any least's he or she is liable to either serving a life sentence or to serving as long a period as ten years imprison with the probable fine imposed by the course. The social activities linked to the transgender are also taken into account. The importance of the Research there are innumerable researches and their results we find now and again in the world of Tamil literatures. Announcing a new truth after a discovery is what we call research. Such a discovery should be useful for the social development. By this reason alone, we can believe that our research is as much important in its merit of dealing with the transgender as they are posing serious social problems today. By this Research work we become able to understand the similarity and disparity between the activities these one groups called as the Transgender rights are granted while the denied their rights. And in such a way this Research is of great importance as it helps us to rightly understand the Transgender in our society.

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Transgender, Tamilnadu, Aravani, Thayamma Nirvana, Koothandavar, ipc 377

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Ayurveda is science which focuses more on prevention of diseases in a healthy individual and later curing of disease in diseased one. In order to prevent any disease, it's very important to know the causative factor (Nidana) of the diseases. Dysmenorrhea being one among the gynecological problems faced by women of reproductive age, prevalence of it varies between 16% - 91% with severe pain in 2% - 29% of women. In ayurveda, dysmenorrhea can be correlated to Udavarthini Yonivyapath. Taking this into consideration, the present study has been entitled "A Pilot Study to Understand Etiology of Udavarthini Yonivyapath (Dysmennorhea)" has been carry out to establish the relation between etiology and menstrual pain, menstrual flow. The etiological factors assessed were mithyachara, beeja dushti and vega dharana. For the present observational study, 50 patients were selected from our institution irrespective of Religion, Occupation and marital status with sign and symptoms of Udavarthini Yonivyapath. The observations obtained are analyzed statically using Spearman's rank correlation coefficient. The results showed that relation between etiology and menstrual flow, pain in Udavarthini Yonivyapath is not statistical significance.

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Nidana, Udavarthini Yonivyapath, Mithyaachara, Beeja dushti, Vega Dharana.

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Review on PIXE: Accelerator Based Analytical Technique

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PIXE, Accelerator, Nuclear Analytical Techniques

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The investigation of peasant's history in India overall and Andhra specifically has not gotten sufficient consideration from the professional scholars. It's kind of research work is a new peculiarity. A large number of the early scholars of history focused to a greater degree toward the ordered picturization of the rulers and privileged in light of accessible inscriptional and historical sources. There are anyway a lot of major obstacles to concentrate peasant history; the greater parts of the accounts are connected with just the main areas of the society, for example, the ruling class.

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Peasants, Inscriptions, Class etc.

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The human face serves as a fundamental and unique identifier, especially in the context of criminal detection and law enforcement. The increasing challenges posed by highly populous urban environments demand automated solutions for efficient and timely identification of individuals. This research introduces an innovative real-time criminal identification system that integrates deep learning, specifically Convolutional Neural Networks (CNN), and Haar Cascade classifier for face detection and recognition. The system utilizes live camera feeds in urban environments, enhancing law enforcement capabilities by combining facial recognition with historical criminal activity data. The proposed approach focuses on extracting detailed facial features through CNN, ensuring robust detection in challenging scenarios. The integration of Haar Cascade enables high-precision real-time face detection. our research contributes to the advancement of criminal identification systems by introducing a real-time approach that harnesses the power of deep learning and live camera feeds. The proposed system holds significant potential for enhancing law enforcement capabilities, enabling proactive identification, and contributing to the overall safety and security of urban environments. As a forward-looking solution, our research not only addresses current challenges but also anticipates future needs in the ongoing evolution of urban security. By incorporating real-time data analytics, our system aids authorities in making informed decisions, reinforcing its role as a proactive and intelligence-driven asset for ensuring public safety.

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Real-time criminal identification, Deep learning, Convolutional Neural Networks (CNN), Haar Cascade classifier, Facial recognition

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ccess to quality health care in rural and underserved areas is often limited, leading to delayed diagnosis and poorer health outcomes. This paper explores the potential of artificial intelligence (AI) to address these healthcare disparities. By analyzing existing literature and research, this article examines how AI can be used to improve the diagnosis of acute diseases in villages and small towns. The article covers data-driven solutions, machine learning and deep learning applications, AI-capable organizations, ethical considerations, and more. The results highlight the transformative potential of AI to bring accurate and accessible diagnosis to underserved areas.

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 Keywords

Challenges, opportunities, AI implementation, rural healthcare, healthcare disparities.

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Emotion recognition from electroencephalography (EEG) signals has become a pivotal aspect of affective computing. This research proposes the concatenation of two novel deep neural network architectures to advance the state-of-the-art in EEG-based emotion classification. The first model termed Hybrid STANN with Graph-Smooth Signals, employs a unique combination of spatiotemporal encoding and recurrent attention network blocks. Graph signal processing tools are applied as a preprocessing step for spatial graph smoothing, enhancing the interpretability of physiological representations. The model outperforms existing methods on the DEAP dataset for emotion classification. Additionally, its robustness is demonstrated through successful transfer learning from DEAP to DREAMER and the Emotional English Word (EEWD) datasets, showcasing its effectiveness across diverse EEG-based emotion classification tasks. The second model, named 3DCANN: Spatio-Temporal Convolution Attention Neural Network, addresses the dynamic nature of EEG signals in emotional states. The 3DCANN model features a spatiotemporal feature extraction module and an EEG channel attention weight learning module. By effectively capturing the dynamic relationships and internal spatial relations among multi-channel EEG signals, the model surpasses state-of-the-art performance on the (SEED) Dataset. The integration of dual attention learning and SoftMax classification enhances the model's ability to discern intricate patterns in EEG signals, resulting in superior emotion recognition accuracy. Both proposed models contribute to EEG-based emotion recognition by introducing innovative architectural elements and demonstrating their efficacy through comprehensive evaluations of diverse datasets. This research opens avenues for further exploration in physiological data-driven affective computing applications.

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 Keywords

Emotion Recognition, Graph Filtering, Spatio-Temporal Encoding, 3D Convolution Attention Neural Network, Dual Attention Learning, Transfer Learning.

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