What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures

Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI\u27s growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem

Analysing the efficacy of training strategies in enhancing productivity and advancement in profession: theoretical analysis in Indian context

An assessment of those needs, also known as a needs analysis, must be carried out in order to ascertain if the organization\u27s requirements, objectives, and concerns can be achieved or addressed via training. In conducting our research, we reviewed training and development-related writing from 1971 to 2023. We believed that the use of more sophisticated training evaluation techniques and statistical approaches, together with an increase in the technological complexity of training design and methodology, set the post-1971 era apart. To be effective, a qualitative review must place more of a focus on qualitative methods of evaluating training effectiveness. Similar to earlier training and development reviews, the present study considered practitioner-oriented literature if it met the criteria listed below for inclusion. A thorough search of the academic literature was conducted to find empirical studies that assessed training programmes or examined the effectiveness of specific training components. After reviewing their abstracts, it was decided to keep 58 articles and papers since they had the proper information. Our research showed that organisations with a strong reputation for employee development are a completely different tale. The majority of businesses monitor the effects of their training efforts in the area of organisational effectiveness. For the second category, increases in productivity, revenue, and profitability are typical signs of organisational success. Overall, there is far more research on team and individual benefits than there is on organisational ones

Some properties of a new subclass of analytic univalent functions defined by multiplier transformation

The purpose of the present paper is to study the integral operator of the form ∫z0{Inμf(t)t}δdt where $f$ belongs to the subclass $C(n,\alpha,\beta, \mu)$ and $\delta$ is a real number. We obtain integral characterization for the subclass $C(n,\alpha,\beta, \mu)$ and also prove distortion, rotation and radii theorem for this class. Relevant connections of the results presented here with various known results are briefly indicated. Mathematics Subject Classification (2010): 30C45, 30C50, 30C55

Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment

Design and Analysis of Slit-cut Stacked Equilateral Triangular Microstrip Patch Antenna

In this paper, a novel structure of slit-cut stacked equilateral triangular microstrip antenna (ETMA) has been theoretically studied using cavity model and found in agreement with the stimulated result calculated by high frequency simulator structure (HFSS). The analysis of stacked ETMA and slit-cut ETMA is done. The theoretical and simulated results of stacked ETMA and slit-cut ETMA are presented and compared. The main concentration is to find the different results, i.e. input impedance and return loss of the slit-cut ETMA and slit-cut stacked ETMA.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.240-244, DOI: http://dx.doi.org/10.14429/dsj.65.797

Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management

In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies. The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors. The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices. In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news

Decision and coordination of WEEE closed-loop supply chain with risk aversion under the cap-and-trade regulation

Driven by the soaring consumption of electrical and electronic equipment (EEE), the semiconductor industry is facing sustainable development challenges such as energy management and carbon emissions. The waste electrical and electronic equipment (WEEE) management practice could contribute to achieving sustainability in the semiconductor industry through the remanufacturing business. Considering the collection quality heterogeneity and its potential influences on the WEEE remanufacturing process, this paper tries to explore the decision-making strategy and coordination mechanism of the WEEE closed-loop supply chain (CLSC) under the cap-and-trade regulation (CATR). The Stackelberg game model is formulated to address this context consisting of a manufacturer with risk avoidance and a retailer with risk neutrality. Besides, we disclose the specific influence of WEEE collection quality on the strategic decision-making of CLSC members under decentralized and centralized modes. Additionally, the revenue and cost-sharing contract is designed to facilitate coordination within the CLSC, and numerical experiments are performed to help better understand the strategic decision-making and effectiveness of the designed contract mechanism. Results show that within a certain threshold of WEEE collection quality, both recycling rate and total profit increase as collection quality improves, while unit wholesale and retail prices of EEE decrease. Conversely, as the risk aversion of the manufacturer increases, the return rate of WEEE decreases, while the wholesale price and retail price per unit of EEE rise. Under the joint influence of the unit carbon emission quota trading price and risk aversion, the unit carbon emission quota trading price exacerbates the impact of manufacturer risk aversion on the return rate. The revenue and cost-sharing contract also contributes to achieving WEEE CLSC coordination under specific conditions

The role of antioxidants and free radicals in the healing effects of Bacopa monniera on acetic acid-induced colitis in rats

Background: The aim to study and elucidate the healing effects of ethanolic extract of dried whole plant of Bacopa monniera against experimental colitis in rats.Methods: Bacopa monniera whole plant extract was administered orally, once daily for 14 days, to rats after induction of colitis with acetic acid. We studied its effects on: faecal output, food and water intake, and body weight changes and also examined colonic mucosal damage, inflammation and status of antioxidants: superoxide dismutase, reduced glutathione; free radicals: nitric oxide, lipid peroxidation on 15th day of the experiment. Antibacterial activity of the extract was also studied using in vitro procedures. Statistical comparison was performed using either unpaired ‘t’ test or one -way analysis of variance (ANOVA) and for multiple comparisons versus control group was done by Dunnett’s test.Results: Bacopa monniera whole plant extract decreased colonic mucosal damage, inflammation, faecal output and increased body weight in acetic acid induced colitis. It also showed antibacterial activity and enhanced the antioxidant but decreased free radicals. Acute toxicity study indicated no mortality or other ANS or CNS related adverse effects even with ten time effective dose indicating its safety.Conclusions: Bacopa monniera whole plant extract is safe, effective and could be beneficial as a complementary agent in treatment of ulcerative colitis

Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency