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Study on Graphs Associated with Groups
In this dissertation, the notions of non-inverse graphs, order sum graphs and coset newlinecomponent graphs associated with groups are introduced. These graphs are simple graphs whose vertices are the elements of the group and the adjacency between the vertices depends on certain properties of the group concerned. Vertices belonging to non-inverse graphs are adjacent if they are not inverses of each other in the group. The vertices in order sum graphs are adjacent if the sum of their orders is strictly greater than the order of the group. The vertices belonging to the coset component graphs are adjacent if their left cosets or right cosets of the subgroups of groups are equal. These algebraic graphs are studied in detail in terms of their structural characteristics, parametric properties and spectral properties. Various characterisations of these graphs are obtained in the study concerned. These notions are further extended to the concept of signed graphs and domination. Properties of signed graphs such as balance, clusterability, consistency, sign-compatibility and so on are investigated for these algebraic signed graphs. The relations between various types of domination are obtained for non-inverse graphs, order sum graphs, complement and line graphs of order sum graphs. -
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Dynamic route scheduler in vehicular ad hoc network for smart crowd control
Revenue generated by tourism is positively correlated with the development of any city. In recent years, tourism is getting peak focus among the government, local bodies, and researchers. This has led to increase in initiatives to grow tourism in and across the country. Being one of the most flourishing sectors, tourism in India shows bold signals of emerging as a strong participant in the world of tourism. In addition to safeguarding its culture and deep-rooted traditional values, tourism provides a way to increase employment opportunities as well as increase the foreign exchange within the country. There are many open research problems arising in the domain, which need the attention of researchers. City traffic management is one among the major concern for cities around the world. Scheduling dynamic travel plans for tourists with crowd and traffic awareness has high scope for research. In this paper, a system is proposed which connects the vehicles to a centralized sink for getting the optimal routes. Route scheduling is done based on a prediction model. Different parameters were collected from the environment that includes crowd, traffic, and schedule of other vehicles. The system has modules like static nodes, mobile nodes, host nodes, and sink node for the control and management. Selection of path and protocol is a primary strategy to design any VANET systems. Hence, performance analysis of routing protocols for the proposed system is done as a major step in selection of protocols. Packet delivery ratio, jitter, and throughput are common measures used for the comparison of protocols. 2019, Springer-Verlag London Ltd., part of Springer Nature. -
Succession planning in India: The path less traversed /
The Management Accountant, Vol.54, Issue 2, pp.30-33, ISSN No: 2581-5504. -
Chemical castration: Justice for victims or justice for the rapist /
American Journal of Criminal Law, Vol.3, pp.1-5, ISSN No: 2581-5504. -
Psychological autopsy: Overview of equivocal deaths, suicides and homicide-suicides
Psychological autopsy studies are a method to understand the causes of equivocal deaths. Suicides and homicide-suicide are the result of various events. The understanding of an individual's life before their death, by interviewing the next of kin, provides some insight into the causes. The research articles dealing with psychological autopsy studies through case studies of suicide or homicide-suicide instances, obtained from different search engines, shed light on the mental health, Alcohol use disorder (AUD), and Drug use disorders (DUD) of individuals before death. The primary characteristic of suicides in a population of late twenties to early fifties was romantic relationship conflicts. In contrast, suicides in the teenage years and early twenties were characterized primarily by a lack of familial acceptability and, to a lesser extent, self-acceptance. Simultaneously, there was a high risk of suicidal behavior and commission of acts among the DUD patients, getting treatment without psychiatric diagnosis and therapy. Over time, the psychological autopsy technique has shown to be quite helpful in determining the risk factors for suicidal behavior. The study helps to develop multiple rehabilitation and mental awareness that need to be created among various populations so that the suicide and homicide-suicide rates can be reduced. 2025 Elsevier Ltd and Faculty of Forensic and Legal Medicine -
Developmental prospects of carrageenan-based wound dressing films: Unveiling techno-functional properties and freeze-drying technology for the development of absorbent films A review
This review explores the intricate wound healing process, emphasizing the critical role of dressing material selection, particularly for chronic wounds with high exudate levels. The aim is to tailor biodegradable dressings for comprehensive healing, focusing on maximizing moisture retention, a vital element for adequate recovery. Researchers are designing advanced wound dressings that enhance techno-functional and bioactive properties, minimizing healing time and ensuring cost-effective care. The study delves into wound dressing materials, highlighting carrageenan biocomposites superior attributes and potential in advancing wound care. Carrageenan's versatility in various biomedical applications demonstrates its potential for tissue repair, bone regeneration, and drug delivery. Ongoing research explores synergistic effects by combining carrageenan with other novel materials, aiming for complete biocompatibility. As innovative solutions emerge, carrageenan-based wound-healing medical devices are poised for global accessibility, addressing challenges associated with the complex wound-healing process. The exceptional physico-mechanical properties of carrageenan make it well-suited for highly exudating wounds, offering a promising avenue to revolutionize wound care through freeze-drying techniques. This thorough approach to evaluating the wound healing effectiveness of carrageenan-based films, particularly emphasizing the development potential of lyophilized films, has the potential to significantly improve the quality of life for patients receiving wound healing treatments. 2024 Elsevier B.V. -
Evaluating the Pertinence of Pose Estimation model for Sign Language Translation
Sign Language is the natural language used by a community that is hearing impaired. It is necessary to convert this language to a commonly understandable form as it is used by a comparatively small part of society. The automatic Sign Language interpreters can convert the signs into text or audio by interpreting the hand movements and the corresponding facial expression. These two modalities work in tandem to give complete meaning to each word. In verbal communication, emotions can be conveyed by changing the tone and pitch of the voice, but in sign language, emotions are expressed using nonmanual movements that include body posture and facial muscle movements. Each such subtle moment should be considered as a feature and extracted using different models. This paper proposes three different models that can be used for varying levels of sign language. The first test was carried out using the Convex Hull-based Sign Language Recognition (SLR) finger spelling sign language, next using a Convolution Neural Network-based Sign Language Recognition (CNN-SLR) for fingerspelling sign language, and finally pose-based SLR for word-level sign language. The experiments show that the pose-based SLR model that captures features using landmark or key points has better SLR accuracy than Convex Hull and CNN-based SLR models. 2023 World Scientific Publishing Europe Ltd. -
Effortless and beneficial processing of natural languages using transformers
Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components. 2022 Taru Publications. -
ML based sign language recognition system
This paper reviews different steps in an automated sign language recognition (SLR) system. Developing a system that can read and interpret a sign must be trained using a large dataset and the best algorithm. As a basic SLR system, an isolated recognition model is developed. The model is based on vision-based isolated hand gesture detection and recognition. Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment. The model made use of a convex hull for feature extraction and KNN for classification. The model yielded 65% accuracy. 2021 IEEE. -
A Comparative Study on Indian Sign Language Representation
Communication among people can happen with the help of verbal or nonverbal language. Nonverbal communication is shared only among the hearing and speech impaired and is not common among others. Non-verbal communication is also different for different countries around the world. A solution to remove the gap between verbal and non-verbal communicators is to create an automated language translation model that can effortlessly convert sign language to text or audio. This area has been under research for a long time, but an economical and robust system that can efficiently convert signs into speech still does not exist. This paper focuses on different approaches that were put forward to turn Indian sign language into audio signals. The Sign Language Recognition (SLR) system is classified as isolated and continuous sign language models based on its input. 2021 IEEE. -
Human Body Pose Estimation and Applications
Human Pose Estimation is one of the challenging yet broadly researched areas. Pose estimation is required in applications that include human activity detection, fall detection, motion capture in AR/VR, etc. Nevertheless, images and videos are required for every application that captures images using a standard RGB camera, without any external devices. This paper presents a real-time approach for sign language detection and recognition in videos using the Holistic pose estimation method of MediaPipe. This Holistic framework detects the movements of multiple modalities-facial expression, hand gesture and body pose, which is the best for the sign language recognition model. The experiment conducted includes five different signers, signing ten distinct words in a natural background. Two signs, 'blank' and 'sad, ' were best recognized by the model. 2021 IEEE. -
A Frame Work For Continous Indian Sign Language Recognition Using Computer Vision
Sign language is a non-vocal, visually oriented natural language used by the hearing newlineimpaired and the hard-for-hearing part of society. It combines multiple modalities newlinelike hand movements, facial expressions and body poses. Static gestures involve basic finger movements such as numbers and alphabets, dynamic signs include words, and a sign sentence consists of grammatically connected and meaningful dynamic words. Sign Language Translation (SLT) models have been an actively evolving research topic under computer vision. One of the most challenging aspects in earlier iterations of SLTs was accurately capturing the intricate and constantly changing hand movements and facial expressions characteristic of sign language. newlineHowever, the advent of deep learning models has facilitated significant advancements in the field, particularly in the realm of continuous sign language translation. newlineThe research endeavours to develop a lightweight deep-learning framework newlinespecifically tailored for the translation of Indian Sign Language (ISL) into text and newlineaudio. The proposed framework introduces two collaborative deep-learning components that extract and classify features synergistically. The ISL video sequence serves as the input, which undergoes feature extraction utilizing the Inception V3 architecture, enabling the extraction of features from each frame. Classification models tend to be bulky and intricate, consuming substantial memory space and requiring extended training periods. This challenge has been addressed by introducing a lightweight LSTM model, which effectively utilizes the feature map generated by the Inception model for accurate classification. It is important to note that each sign possesses unique characteristics yet exhibits similar feature maps. The performance of the framework is assessed based on the speed and accuracy achieved in converting the input video into text and audio formats. -
A Mixed methods study of psyhosocial factors in career decision making in adolescents
Career choice is an important developmental task in adolescence and is influenced by many factors. Using a mixed methods research design, this study aimed to understand career decision making and factors influencing the same in adolescents. In the quantitative phase the relationship between career maturity and perceived parenting style, personality traits, metacognition, socio- economic status, gender, college type, stream of study and decision status was studied in students studying in II Year Pre- University in Bangalore, India. Career decisions, personal and family factors in career decision making were explored in the qualitative phase. Informed consent was obtained from the participants and parents of the participants of the study. newlineQuantitative data was collected from 548 students studying in Arts, Science and Commerce stream in second year Pre- University in Bangalore. Students from eight private and seven government colleges were recruited for the study. Quantitative data was collected using a socio- demographic data sheet, Career Maturity Inventory, Parental Authority Questionnaire, Neo Five Factor Inventory and Metacognitive Awareness Inventory. The scales were translated to Kannada and back translated. In the qualitative phase, data was collected through a semi- structured interview schedule designed for this study. 30 students who were a part of the quantitative phase took part in this phase. The interviews were audio-recorded and transcribed for analysis. Statistical analysis was done to analyze quantitative data. Descriptive statistics, correlation, regression analysis, t tests and one-way ANOVA was done. Qualitative data was analyzed by template analysis and themes were derived from the data. The results revealed associations between personality traits neuroticism, openness and conscientiousness and specific aspects of career maturity attitude and competence. -
Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE. -
Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models
The dynamics of campus placements have garnered considerable attention in recent years, with educational institutions, students, and employers all keenly invested in understanding the factors that drive successful recruitment. This surge in interest stems from the potential implications for academic curricula, student preparation, and hiring strategies. In this study, we aimed to unravel the myriad factors that influence a student's placement success, drawing from a comprehensive dataset detailing a range of academic and demographic attributes. Our methodology combined thorough exploratory data analysis with advanced predictive modeling. The exploratory phase unveiled notable patterns, particularly highlighting the roles of gender, academic performance analysis, Degree and MBA specialization in placement outcomes. In the predictive modeling phase, the spotlight was on state-of-the-art machine learning models, with a particular emphasis on their capacity to forecast placement success. Notably, algorithms like Logistic Regression and Support Vector Machines not only confirmed the insights from our exploratory analysis but also showcased remarkable predictive prowess, with accuracy scores nearing perfection. These findings not only demonstrate the capabilities of machine learning in the academic and recruitment spheres but also emphasize the enduring importance of core academic achievements in influencing placement outcomes. As a prospective direction, future research might benefit from examining how placement trends evolve over time and integrating qualitative insights to provide a holistic view of the campus recruitment process. 2023 IEEE. -
Exploring Machine Learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka
In contrast to gold and platinum, whose values may be fairly determined, determining a diamond's worth involves a far more complex set of considerations. The appropriate rate is based on many factors, not just one of the stones. Diamonds are graded based on their appearance, carat weight, cut quality, and how well they have presented dimensions like a table's surface, depth, and breadth. In order to accurately forecast diamond prices, this study seeks to develop the most effective approaches possible. Different machine learning classifiers are trained on the diamond dataset to forecast diamond prices based on the features. This article shows how to analyze diamond prices using WEKA's data mining software. Diamond data have been utilized for this study. These methods include M5P, Random Forest, Multilayer perceptron, Decision Stump, REP Trees, and M5Rules. For the purpose of estimating the cost of a diamond, different Machine Learning classifiers are compared and contrasted. Performance measures and analysis showed that Random Forest was the best-performing classifier. Experimental findings show, as shown by the coefficient of correlation that Random Forest is better than other classification methods. 2023 IEEE. -
Insider attack detection using deep belief neural network in cloud computing
Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine. 2022 CRL Publishing. All rights reserved.