Journal of Interconnection Networks

Corporate Industries apply new technologies for manufacturing and production applications. This makes the process depend upon multiple computers, robotic applications, and varying specifications, which reduces efficiency and speed. These technological challenges are incredibly diverse since an extensive range of processing technologies is available. When it comes to human-centered automation, it’s all about the scientific knowledge and data encapsulation for robots to interact within this field. Robots must operate in various ecosystems and interact closely with non-professional customers. Current technology is not well adapted to this scenario, requiring sustainable management separated from available technology. In this article, the human-centered Industrial Robot using Artificial Intelligence (AI-HCIR) is suggested as a tool to resolve such issues. Several manufacturing protocols are presented to efficiently produce products that simplify human work and involve different needs. The HCIR scrutinizes it for time allocation and manufacturing time allocation. For this, a suitable processing environment and a detailed simulation is conducted. The proposed AI-HCIR achieves a 33.68% error rate, 24% throughput, and 18.7% efficiency in the smart city environment. In contrast with conventional methods, the proposed method obtains a better result efficiently.

The identification of speech emotions is amongst the most strenuous and fascinating fields of machine learning science. In this article, Chinese emotions are classified as a disruptive atmosphere that classifies several feelings into four major emotional organizations: pleasure, sorrow, resentment, and neutrality. A machine learning in human emotion detection (ML-HED) framework is proposed. The technology suggested removing prosodic and spectrum elements of an audio wave, such as a pulse, power, amplitude, Cepstrum melt frequency correlations, linearly fixed Cepstral, and identification with a template. In all, 87,75% of performers’ statements and 93% of women’s actors were given reliability. The research findings show that the revolutionary technology achieves greater precision by accurately interpreting the feelings, which contrasts with current speech emotion recognition approaches. Besides, the derived characteristics were contrasting with various classification techniques in this study for the comprehensive idea.

Group work can inspire students, encourage constructive learning, and improve essential critical thinking, communication, and decision-making in the present competitive world. The risk factors in group learning include students who prefer working alone and strongly despise dealing with things created by gathering in teams. Online learners often have problems locating lasting peace times for group therapy sessions are considered an essential factor. A predictive Group Learning Behavior Approach (PGLBA) has been proposed. Students who commute to college agree to group conferences and workgroup learning and Inquiry learning for higher education. The grounded Inquiry Learning Approach is invented to strengthen students’ enjoyment of active group learning, and the students find times for group meetings that are often mutually advantageous. The simulation analysis is performed based on performance, accuracy, and efficiency proves the proposed framework’s reliability. The experimental results show that the proposed PGLBA-IL model enhances the accuracy ratio of 81.2%, an efficiency ratio of the number of students 86.4%, and the overall performance analysis ratio of 85.1% compared to others existing approaches.

Virtual reality (VR) has advanced as a collaborative, realistic, and creative computation technique in recent decades. With organizations becoming digitally more focused and employees’ experience changed by technology, manager’s face and continue to confront several obstacles in the digital transformation process. Recent advances in information integration have been made possible by implementing the improved digital twin (DT) paradigm and its use in the workspace. To solve these problems, simulated convergence, realistic dynamic computational decision-making, and other tools are effective. This helps to complete activities with physical models and records. Thereby, this paper presents a Visually Improved Digital Media Communication Framework (VIDMCF) using VR technology and DT. Incorporating all information, displaying the whole procedure, avoiding challenges, closing loops, optimizing repetitive processes, and making complex decisions in real-time can be aided by reproducing physical systems in the virtual design and adding VR and digital mirror twin to the output of digital media. The proposed model can achieve connectivity and convergence among the realistic atmosphere and the digital environment’s virtual system in cyber-real-space harmony over the life cycle.

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

Energy consumption is important to consume less power, reducing toxic fumes released by plants, preserving natural resources, and protecting ecosystems against damage. The challenging characteristics in energy supply include lack of renewable energy adoption, and policy and energy management are 0considered essential factors. An artificial intelligent building with a multi-energy planning method (AIBMEM) has been proposed to design multi-energy systems to achieve the best policy and energy management techniques. The intelligent construction problem with multi-energy is framed as a predictive energy model to minimize the overall utilization of energy levels. The normal distribution with the artificial intelligent model is introduced to solve the problem of renewable energy. The experimental results based on reliability, effectiveness, preservation, energy consumption, and control systems show that the suggested model is better than existing models, producing good performance analysis results.

Energy consumption in smart cities relates to every energy consumed to carry out an activity, produce something, or exist in a structure. The most common measurement of energy efficiency is energy consumption per square meter in city residential areas. The states’ problematic energy consumption characteristics in smart cities may include climatic change, rainfall issues, water scarcity, and electricity generation. Thus, based on the states of households, an expanded proposed system of statistic determination impact conversion by positive, accurate technology (STIRPAT) model has been developed. STIRPAT model is collaborative research that aims to learn about the dynamic connections between human systems and the surrounding environment. There are two methods of the STIRPAT model to satisfy the characteristic of the proposed approach. The energetic counseling framework is an emerging technique that overcomes climatic change, electricity generation, and rainfall issues by sensing it in the environment. An algorithmic approach of the standard genetic method offers to conclude the problems into a cloud block mechanism for visualizing the states. Thus, the integrated technique of these two methods shows the factual implementation to overcome the statistical problems. Further research shows that, since the significant effect had been taken into account, the energy consumption per square meter in metropolitan residential buildings peak occurred eleven years later than without considering the dilution effect. The performance ratio of the STIRPAT model is estimated to be 98.3% by comparing with overall researches.

Intelligent transportation systems (ITS) are a collection of technologies that can enhance transport networks and public transit and individual decision-making about various elements of travel. ITS technologies comprise cutting-edge wireless, electronic and automated technology intending to improve safety, efficiency and convenience in surface transit. In certain cases, reducing energy usage has proven to be an ITS advantage. In this report, the primary energy advantages of a range of ITS systems established through models, pilot projects/field tests and extensive use are examined and summarized. In worldwide driving, the Internet of Things (IoT) solutions play a vital role. A new age of communication leading to ITS will be the communication between cars via IoT. IoT is a mixture of data and data analysis data storage and processing to manage the traffic system efficiently. Energy management, which is seen as an efficient, innovative approach to highly efficient energy generation plants. It simultaneously takes care of optimizing traditional sources of the IoT based intelligent transport system, helps to automate railways, roads, airways and shipways, which improve customer experience in the process. Following an evaluation of the situation, a proposal named energy management in intelligent transportation (EMIT) improves energy efficiency and economic efficiency in transportation. It improves energy management to reduce economic and ecological waste by decreasing global transport energy consumption. The sustainable development ratio is 85.7%, accidents detection ratio is 85.3%, electric vehicle infrastructure ratio is 83.6%, intelligent vehicle parking system acceptance ratio is 82.15%, and reduction ratio of energy consumption is 91.4%.

This paper introduces the mobile search algorithm into the research of employment accessibility measurement. It not only proves the effectiveness of the method, but also analyzes the characteristics and formation mechanism of College Students’ employment accessibility in the research case, and puts forward the corresponding control strategies. The results show that the employment accessibility of college students is not evenly distributed, but presents an obvious center periphery pattern. This paper puts forward the planning means of the accessibility of College Students’ employment education resources to promote the balanced distribution of employment and population, the reform of the employment structure and the reasonable improvement of policy.

In the process of learning, learners will express their emotions through a variety of forms, facial expressions and voice are more obvious, which are most easily obtained through computers. In the previous methods, it is mainly based on a single modal, such as expression, speech, text and so on. Due to the diversity of information, the accuracy rate of multimodal recognition is higher than that of single modal recognition. Therefore, this paper proposed a DNN-based multimodal learning emotion analysis method which combines video and speech to detect students’ learning emotion in real time. We use this method to automatically identify learning emotions in primary school English classroom. According to different learning emotions, the PAD emotion scale was used to correspond learning emotions with learning states. Teachers can judge students’ learning state according to the change of students’ learning emotions, so as to adjust teaching methods and strategies in time.

The number of autistic children and young people is rising rapidly across the world. Children with intellectual disabilities need special attention from trained experts. Educating them on improving their lifestyle is critical through the traditional teaching-learning environment. This study introduces an interactive educational framework that helps children with special needs have an improved and exciting learning process and explores the need to incorporate physical exercise into their everyday lives. Virtual Reality (VR) seeks more attention from autistic students. This research presents a Machine Learning-based Virtual Reality Application (ML-VRA) for Mentally Challenged Children and keeps the Human Behavior Analysis log files. Machine learning can predict the score of brain data ability. The visual short-term memory and visual-spatial memory are further assessed to identify students’ interaction with the VR application. Support Vector Regression prediction algorithm and Baseline Prediction algorithm are used to assess the score prediction for visual short memory and visual-spatial memory. Using an audio technology that allows autistic persons to hear various sounds, the cognitive method VRA instructs autistic children. Further, this study proposes a cognitive model for intellectual task processes and problem-solving using metacognitive architecture. Thus, children can acquire different levels of learning knowledge and skills. The case study performed on these model results with the highest prediction accuracy of 93.65%.