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This study introduces a novel topological descriptor, the second Davan index (SDI) based on weighted degree of molecular graphs. Its chemical significance is validated through QSPR modelling of octane isomers, where it exhibits superior correlation with physico-chemical properties such as entropy, acentric factor, density and molar volume, outperforming classical indices like the Sombor index, second hyper Zagreb index and redefined third Zagreb index. The second Davan index demonstrates enhanced isomer discrimination capability, as evidenced by its high sensitivity values compared to established descriptors. Further, bounds are established for connected graphs and closed form expressions are computed for standard graph classes and certain nanostructures.

The current review focuses on how artificial intelligence (AI) and robotics can be applied to the field of Prosthodontics and oral implantology. The classification and methodologies of AI and application of AI and robotics in various aspects of Prosthodontics is summarized. The role of AI has potentially expanded in dentistry. It plays a vital role in data management, diagnosis, and treatment planning and administrative tasks. It has widespread applications in Prosthodontics owing to its immense diagnostic capability and possible therapeutic application. AI and robotics are next-generation technologies that are opening new avenues of growth and exploration for Prosthodontics. The current surge in digital human-centered automation has greatly benefited the dental field, as it transforms towards a new robotic, machine learning, and artificial intelligence era. The application of robotics and AI in the dental field aims to improve dependability, accuracy, precision, and efficiency by enabling the widespread adoption of cutting-edge dental technologies in future. Hence, the objective of the current review was to represent literature relevant to the applications of robotics and AI and in the context of diagnosis and clinical decision-making and predict successful treatment in Prosthodontics and oral implantology.

Breast cancer (BC) disease is the most common and rapidly spreading disease across the globe. This disease can be prevented if identified early, and this eventually reduces the death rate. Machine learning (ML) is the most frequently utilized technology in research. Cancer patients can benefit from early detection and diagnosis. Using machine learning approaches, this research proposes an improved way of detecting breast cancer. To deal with the problem of imbalanced data in the class and noise, the Synthetic Minority Oversampling Technique (SMOTE) has been used. There are two steps in the suggested task. In the first phase, SMOTE is utilized to decrease the influence of imbalance data issues, and subsequently, in the next phase, data is classified using the Naive Bayes classifier, decision trees classifier, Random Forest, and their ensembles. According to the experimental analysis, the XGBoost-Random Forest ensemble classifier outperforms with 98.20% accuracy in the early detection of breast cancer.

Soil testing is a unique tool for finding the available soil reaction (pH), organic carbon, and nutrients status of the soil. It helps to select the suitable crops concerning available pH and soil nutrients level to increase crop production. In this current approach, the soil test prediction is used to differentiate several soil features like soil fertility indices of available pH, organic carbon, electrical conductivity, macro nutrients, and micro nutrients. The Classification and prediction of the soil parameters lead to reduce the artificial fertilizer inputs, increasing crop yield, improves soil health and crop growth and increase profitability. These problems are solved by using fast learning and classification techniques known as machine learning (ML) classifier techniques such as random forest, Gaussian naïve Bayes, logistic Regression, decision tree, k-nearest neighbour and support vector machine. After the analysis decision tree classifier attains the maximum performance to solve all problems which goes above 80% followed by other classifiers.

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids’ selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm’s usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines.

Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

Introduction: Dental anxiety proves to be the hurdle for dental care, making self-awareness among the population more crucial. Similarly, bruxism has also been reported to be due to stress, but the pathophysiology has not been clearly understood. The current research aims to explore the association of personality traits with bruxism and dental anxiety among health professional students.Methods: A total of 120 dental and medical students were included in our study. All the participants received three different questionnaires: The “modified dental anxiety scale” questionnaire to measure dental anxiety, the “modified bruxism assessment questionnaire” to assess the presence of bruxism, and “the big five inventory” to identify the personality trait. The collected data were statistically evaluated with significance at p < 0.05.Results: Comparison of dental anxiety among professional students showed significantly (p < 0.001) higher anxiety among medical than dental students. Analyzing the prevalence of bruxism revealed awake bruxism to be significantly (p < 0.05) higher in males than females. On analyzing the relation between personality traits and dental anxiety, a positive correlation was seen between the neuroticism type of personality (r = 0.193, p < 0.05) and dental anxiety, especially in females.Conclusion: The prevalence of self-reported awake bruxism was higher among male students, indicating the necessity for more investigation to ascertain the influence of various psychological factors. The correlation between dental anxiety and neuroticism type of personality trait points out the importance of identifying these individuals in a clinical setting and implementing strategies to reduce anxiety and enhance motivation for treatment.

Nano technology is the science and engineering related to particles sized 10-9 of a meter. Albeit, the small size, they have opened up a huge array of possibilities for this world. By the inclusion of nanoparticles in various materials not only, the field of engineering but also, medical and dentistry have been benefitted largely. Researchers have manipulated the particles at a molecular and atomic level, which have opened up huge possibilities for the same material. The recent advances have helped to achieve accurate and fast diagnosis, helped to prolong the longevity of dental materials and hence helped to create a healthy oral environment. However, with all these developments, it is important that we focus on the shortcoming or the hazards too and carefully comply with the same before its long-term application. Therefore, this article focuses on the basis, all the present applications of Nano dentistry and the potential it holds for the field of dentistry in future.

Pilocarpine (Salagen) and Cevimeline hydrochloride (Evoxac), increases salivary flow for procedures of a short-duration (3-hour), indicating a certain degree of selectivity at salivary cholinergic receptors without accompanying side effects. Analgesics: The nonsteroidal anti-inflammatory drugs (NSAIDs) are the commonly used analgesics in Prosthodontics, which is used to manage pain during the surgical stage of the implant placement. Prophylactic antibiotics are generally prescribed to prevent the onset of infection at the site of implant placement by elevating the antibiotic concentration in the blood, thereby reducing the chances of bacterial proliferation and dissemination. Nystatin tablet is held in mouth until they dissolve, as it not absorbed, dentures, which are colonized with candida albicans may be treated by soaking them in a nystatin solution.

With the development of more compact and powerful methods of designing a digital logic on a silicon chip, most of the signal processing is being implemented in the digital domain. The implementation of an efficient reconfigurable digital decimation filter is presented in the work. In this paper we focuses with the implementation and design of a decimation filter which is used for hearing aid applications. We design decimation filter with the help of the canonic signed digit (CSD) representation. In decimation filter the cascaded integrated comb filter is designed using without multiplier less. The half band and corrector filters are designed using CSD. The decimation filter has been implemented on Xilinx FPGA using Virtex-2 technology and number of slices ,number of LUTs and number of registers are reported and also proposed design is implemented in synopsis design compiler for ASIC implementation and calculated area, power and delay. The resulting architecture is hardware efficient and consumes less power compared to conventional decimation filters. Compared to the normal decimation filter architecture, the proposed decimation filter architecture has less hardware saving of 60% and in addition, it decreases the power consumption of 80%, respectively and the proposed architecture is well suited for decimation filters of the hearing aids.