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Digital marketing communication involves the strategic use of various online channels and platforms to convey messages, promote products or services, and engage with target audiences. This multifaceted approach includes strategies such as search engine optimization (SEO), content marketing, social media marketing, email marketing, pay-per-click (PPC) advertising, display advertising, mobile marketing, video marketing, and data analytics. By using these digital channels effectively, businesses can increase brand visibility, drive website traffic, generate leads and ultimately increase sales. Success in digital marketing communications is centered on understanding audience preferences, crafting compelling content, using data insights to optimize, and maintaining a dynamic online presence that resonates with customers. Gen Z, also known as Zoomers, refers to the demographic group born roughly between 1996 and 2010s, succeeding Millennials and preceding Generation Alpha. This generation is characterized by having grown up in a digitally connected world where smartphones, social media and instant access to information are common aspects of their lives from a young age. Gen Z individuals are often described as technological, socially conscious, with a preference for authenticity and meaningful experiences. Understanding the unique characteristics, preferences and behaviours of Gen Z is essential for businesses and marketers looking to engage with this generation effectively. The present study is descriptive in nature. Data has been collected from both Primary & Secondary sources. Data has been collected from 81 respondents. Descriptive statistics, Chi-square test & Fried man Ranking were used for the present study. The study concluded that there is a significant impact on digital marketing communication on Gen Z buying behaviour. [ABSTRACT FROM AUTHOR]

Machine learning has the potential to improve healthcare by allowing clinicians to spend more time caring for patients and less time diagnosing them. This would allow clinicians to spend more time improving patient quality of life. Consequently, it is able to compute the risk of melanoma on a patient level and advise users to schedule a medical checkup rather than evaluating whether or not a specific lesion image that is provided by a patient is malignant. This is because the result of this is that it is able to compute the risk of melanoma at the patient level. By doing so, both the credibility and legislation issues are resolved, and the application is transformed into one that is adaptable. In this paper, we develop a machine learning ensemble to classify the melanoma cancer. The simulation is conducted in terms of training, testing accuracy, precision and recall. The results show that the proposed method achieves higher classification rate than other methods. [ABSTRACT FROM AUTHOR]

Cyber bullying, which is a critical global issue that affects both individual victims and societies in a large number. Cyber bullying refers to the use of electronic communication to bully a person, typically by sending messages of an intimidating or threatening nature. Harmful bullying behaviour can include posting rumours, threats, sexual remarks, a victims' personal information, or pejorative labels. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyber bullying; however, because these attempts rely on the victims' interactions, they are practical. Therefore, detection of cyber bullying without the involvement of the victims is necessary. Machine Learning Concepts are used for the implementation of the project which aims to classify the input into bullying or non-bullying statements. This paper detects cyber bullying in tweets using ML Classification Algorithms. Each of these algorithms are evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers' recognition rates applied to the dataset. Training and predicting pipeline is implemented to contrast performance of various classification algorithms and determine the best suited model. It also shows that only the non-bullying tweets can be posted by the user, and any bullying content is discarded and hence not posted. [ABSTRACT FROM AUTHOR]

Within the field of natural language processing, sentiment analysis is one form of data mining used to make inferences about the emotional tenor of a speakers words. Computational linguistics is employed to examine the text in order to deduce and assess ones mental knowledge of the Web, social media, and associated references. One of the numerous advantages of sentiment analysis is that it can help improve the quality of healthcare by making use of medical data to produce the most positive outcome possible. Natural language processing challenges can change how sentiment analysis looks and works in a variety of contexts. Some of the challenges are specific to the data type, while others are universal to any method of text analysis. The primary objective of this study was to evaluate how challenging it is to analyse sentiment in the healthcare sector. Given the aforementioned complexities, the objective was to look into whether or not the currently available SA tools are adequate for handling any healthcare-related issue. With such motivation, in this paper, we develop an unsupervised ensemble machine learning (ML) algorithm that includes K-means clustering; Principle Component Analysis; Independent Component Analysis and k-nearest neighbors. The unsupervised ensemble ML model is assessed via voting meta-classifier over various cancer datasets. The simulation is conducted to test the efficacy of the model in terms of accuracy, precision, recall and f-measure over various datasets. The results of simulation against the cancer datasets show that the proposed method achieves higher rate of ensemble accuracy than the other existing ensemble models. [ABSTRACT FROM AUTHOR]

Biosurfactant produced by Pseudomonas putida was isolated from the oil contaminated soil and its biochemical characterization were analyzed and estimated as 1mg of biosurfactant contains 1.74% of carbohydrate, 12.5% of lipid and 5.7% of protein, and characterization of biosurfactant like FTIR, NMR & ESI-MS were determined to identify the specific compound, the results inferred that rhamnolipid like structure has been identified and its application work has been carried out using hydroponic method. [ABSTRACT FROM AUTHOR]

Byline: M. Uma Maheswari, P. Rao OBJECTIVES: To study the effect of oral administration of grape seed oil (GSO) against carbontetrachloride (CCl4)-induced hepatotoxicity in rats. METHODS: Liver damage was induced [...]

Background: The physiologically mobile natural tooth and rigidly fixed dental implant causes different distribution of stress when connected in prosthesis and nonrigid connector compensates this. Understanding of biomechanical behavior is necessary for an adequate choice and construction of this type of rehabilitation. However, there has been insufficient research focusing on different location and type of the nonrigid connector related with the prognosis of both implant and the tooth.Aim of the Study: The purpose of this finite element (FE) analysis was to evaluate the stress distribution around bone, implant, and tooth in tooth implant fixed prosthesis under static load with variations in design and location of nonrigid connectors under simulated functional loads.Materials and Method: Three, 3-dimensional FE models connecting tooth and implant were constructed with different location and type of nonrigid connector. Simulated occlusal load was applied on the restorations and stresses developed in the supporting structures were monitored.Results: The highest stresses were found around the implant in model with nonrigid connector placed between the tooth and implant and model with modified nonrigid connector. On the other hand, less stress was noted around the implant where nonrigid connector was placed between the implant and pontic.Conclusion: It is advisable to place the nonrigid connector between the implant and the pontic to protect the implant from torque effects in a tooth implant fixed prosthesis. [ABSTRACT FROM AUTHOR]

The study of consumer behaviour is essential in the changing scenario of the Indian consumer market. In recent times, the orientation of rural consumers has turned towards acquiring specialty goods like automobiles. Comprehending the patterns and understanding the rural consumer behaviour is a challenge. The study focuses on pre-purchase information search behaviour of rural pre-owned car owners in Kanniyakumari district, Tamil Nadu. Burgeoning growth of the new car market and the reduced tenure of car ownership resulted in wider availability of used cars. This study is significant as the pre-owned car consumers are potential new car buyers and their experience with a pre-owned car will have a definitive influence on future car purchase decisions. The study has been undertaken with two objectives; to assess the socio-economic and demographic profile of rural pre-owned car owners in Kanniyakumari district and to examine the pre-purchase information search behaviour of rural car owners. Primary and secondary data have been used. The primary data have been collected through structured questionnaire. Judgement sampling method was adopted to select 200 used car owners. Descriptive statistics and Garrett's ranking technique were used for analysis. The pre-owned car market in the study area is dominated by individuals. Pre-owned car was bought due to lower price after brand evaluation but with apprehension on mind. The pre-owned car market can be made organized by proper registration of dealers. To overcome the challenges in reaching rural consumers, the car marketers can formulate effective rural marketing strategies. [ABSTRACT FROM AUTHOR]

The theoretical model for an amperometric glucose biosensor is discussed. In this model glucose oxidase enzyme is immobilized in conducting polypyrrole. This model contains a nonlinear term related to enzyme reaction kinetics. He's homotopy perturbation method is used to find the approximate analytical solutions of coupled non-linear reaction diffusion equations. A closed-form expression of substrate and mediator concentration under non-steady-state conditions is obtained. A comparison of the analytical approximation and numerical simulation is also presented. An agreement between analytical expressions and numerical results is observed. [ABSTRACT FROM AUTHOR]

The present investigation was aimed to study the antidiabetic, antihyperlipidemic, and in vivo antioxidant properties of the root of Sphaeranthus indicus Linn. in streptozotocin- (STZ-) induced type 1 diabetic rats. Administration of ethanolic extract of Sphaeranthus indicus root (EESIR) 100 and 200 mg/kg to the STZ-induced diabetic rats showed significant (P < .01) reduction in blood glucose and increase in body weight compared to diabetic control rats. Both the doses of EESIR-treated diabetic rats showed significant (P < .01) alteration in elevated lipid profile levels than diabetic control rats. The EESIR treatment in diabetic rats produced significant increase in superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and decrease in thiobarbituric acid reactive substances (TBARS) levels than diabetic control rats. Administration of EESIR 200 mg/kg produced significant (P < .01) higher antioxidant activity than EESIR 100 mg/kg. The high performance liquid chromatography (HPLC) analysis of EESIR revealed the presence of biomarkers gallic acid and quercetin. In conclusion, EESIR possess antidiabetic, antihyperlipidemic, and in vivo antioxidant activity in type 1 diabetic rats. Its antioxidant and lipid lowering effect will help to prevent diabetic complications, and these actions are possibly due to presence of above biomarkers. [ABSTRACT FROM AUTHOR]