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Gender Recognition (GR) is the process of identifying gender difference by extracting and evaluating features from data existing as images, video, audio, text, and other signals. The task of GR has been achieved using face, keystroke, gait, and speech features. "Laughter" with its intrinsic usefulness for this task is yet to be explored. Laughter is an important paralinguistic feature in human communication. It can change the meaning of speech when triggered by some form of arousal or amusement. Although, laughter can be "acted," but the human natural laughter is a spontaneous reflex response, which reasonably embeds some characteristic peculiarities of the individuals. The human brain has capacity to make this distinction. In this study, spontaneous laughter bouts of 123 volunteers (41 females and 82 males) were recorded. The Dynamic-Average of the Mel frequency Cepstral Coefficient (DA-MFCC) were generated and trained using two conventional and effective machine learning algorithms that have been employed in gender identification. These algorithms are Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers. Overall accuracies of 87.65% and 86.91% were obtained with GMM and SVM respectively. Therefore, indicating the possibility of using laughter characteristics as signatures for distinguishing between male and female genders. For both classifiers, the use of DA-MFCC reasonably reduced training time. Some of the potential areas of applications of GR include security, health care, marketing, human machine interaction toward enhanced emotion recognition, automatic speaker recognition and forensics.

The competitive nature of today's economies has forced the manufacturing sector, small and medium-sized manufacturing enterprises (SMMES), to collaborate with other sectors to achieve stability and consistency. Manufacturing businesses are putting a lot of effort into managing their goods and services to reach a high level of client satisfaction. This is accomplished with the highest quality while maintaining a competitive cost tag. To accomplish this, the collaborative manufacturing technique (CMT) is used. It entails information exchange and dialogue amongst business processes concerning internal or external stakeholders in the hierarchy of value. An active CMT model that incorporates these present collaboration networks should provide operational value savings and significantly increase competitiveness. Therefore, the recent evaluation that provided a detailed view of such inclusion is no longer in existence. To promote collaboration techniques in the successful development of software products, this article provides a complete study of current collaborative models, respective benefits, and their collaborative features. This paper outlines the most recent mechanisms, approaches, and application possibilities for CMTs. In addition, the review paper thoroughly examines the techniques currently in use for employing CMT to solve problems in both science and engineering. More specifically, it suggests a revolutionary method for enhancing the current CMT methods through the application of machine learning (ML), artificial intelligence, and metaheuristics like genetic algorithms and particle swarm optimization (AI). In summary, this research highlights certain areas where CMT may be used soon.

Taking attendance for classes has become more challenging, especially at institutions, due to the growing students population. Typically, the teacher calls names and writes them down. This approach has a number of drawbacks because when recording their attendance, students are diverted from the presentation and may register for absent classmates. The smart attendance solution suggested in this study allows students to tap their student identity (ID) card on a near field communication (NFC) reader, streamlining the attendance procedure by using near field communication (NFC) technology. The technique is affordable, effective, and simple to keep up. Automating student attendance saves time and alleviates anxiety. The average time for the NFC system to take a student's attendance is 3.5 seconds, compared to 16 seconds for the conventional method. The inaccuracy associated with recording attendance is reduced by 31% thanks to NFC technology. The numerical results of the NFC-based scheme are compared with the traditional scheme and observed that the proposed scheme outperforms the conventional scheme.