A rising proportion of older people has more demand on services including hospitals, retirement homes, and assisted living facilities. Regaining control of this population’s expectations will pose new difficulties for lawmakers, medical professionals, and the society at large. Smart technology can help older people to have independent and fulfilling lives while still living safely and securely in the community. In the last several decades, the number of sectors using robots has risen. Comrade robots have made their appearance in old human life, with the most recent notable appearance being in their care. The number of elderly individuals is increasing dramatically throughout the globe. The source of the story is the use of robots to help elderly people with day-to-day activities. Speech data and facial recognition model are done with AI model. Here, with the Comrade robotic model, elder people’s healthcare system is designed with better analysis state. The aim is to put in place a simple robotic buddy to determine the health of the old person via a headband that has been given to them. Comrade robot may do things like senior citizens home automation, home equipment control, safety, and wellbeing sensing, and, in emergency situation, routine duties like navigating in the outside world. The fear that robotics and artificial intelligence would eventually eliminate most of the jobs is increasing. It is anticipated that, in order to survive and stay relevant in the constantly shifting environment of work, workers of the future will need to be creative and versatile and prepared to identify new business possibilities and change industry to meet challenges of the world. According to the research, reflective practice, time management, communicating, and collaboration are important in fostering creativity.

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Internet‐of‐Things (IoT) enabled cyber‐physical systems (CPS) is a system in which communication between the physical devices and the cyber environment runs independently without any user interaction. Several optimization algorithms have been used for determining the optimal solutions that can reduce the production cost and/or enhance the production efficiency with in limited time‐periods. However, existing optimization approaches have failed to solve the issues in the complex manufacturing process. To overcome this issue, a novel technique called directed acyclic graph theory based multiobjective oppositional learnt artificial ant colony resource optimization (DAGT‐MOLAACRO) technique has been introduced in this study for solving the complex manufacturing process in the industry. Initially, IoT devices are used in the industrial sector for sensing and collecting data. Then the collected data is sent to the cyberspace of the CPS system with the least latency. Then, the CPS system collects the data generated from the industrial IoT devices that is stored in cyberspace with lesser memory consumption. MOLAACRO is applied to find the optimal solution among the population that satisfies the resource constraints by constructing the directed acyclic graph. In this way, the DAGT‐MOLAACRO technique reduces the time complexity with minimal latency and computation overhead. For verification purposes, our experimental work has been carried out using different performance metrics such as data latency, time complexity, and computation overhead with respect to the number of IoT devices and the amount of data collected. The results show that the DAGT‐MOLAACRO technique has better performance with reductions in terms of time complexity by 10%, latency by 17%, and the computation overhead by 11% against the existing works in literature.

The speaker diarization is the process of segmentation and the grouping of the input speech signal into a region based on the identity of the speaker. The main challenge in the speaker diarization method is improving the readability of the speech transcription. Hence, in order to overcome the challenge, a speaker diarization method based on deep LSTM is proposed in this research. Initially, the pre-processing is performed for the removal of the noise from the audio lecturing of E-Khoolusers. Then, Linear Predictive Coding (LPC) is used for the extraction of the efficient features from the audio lectures of the E-Khoolusers. After the extraction of the features, the absence or presence of the speaker in the audio lecture is detected using the VAD technique which is followed by the segmentation of the speaker using the extracted features. Finally, the feature vector is determined and the speaker from the audio lecturing of the E-Khoolusers is clustered using the deepLSTM. The proposed speaker diarization method based on deep LSTM is evaluated using the metrics, such as sensitivity, accuracy and specificity. When compared with the existing speaker diarization methods, the proposed speaker diarization method based on deep LSTM obtained a minimum DER of 0.0623, minimum false alarm rate of 0.0369, and minimum distance of 2546 for varying frame length and obtained a minimum DER of 0.0923, minimum false alarm rate of 0.0869, and minimum distance of 1146 for varying Lambda.

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