Journal of Interconnection Networks

This study is an empirical analysis of China’s “sustainable development ability of water cultural industry” based on the attributes of water cultural industry (economic attribute and cultural ideology). First, using “factor analysis” and “grey relational analysis theory,” establish an indicator-system for the level of sustainable development of the cultural industry, then quantify the relationships between the water cultural industry and its influencing factors, and finally, propose some solutions for enhancing the sustainable development of the water cultural industry from the perspective of “improving resiliency.”

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

In recent studies, YOLOv3, a deep learning-based target detection algorithm, becomes extensively used in object recognition, especially guiding the visually disabled. Current YOLOv3-based assistive technology for the disabled person can now achieve high-precision, real-time object recognition. Even though this algorithm has several flaws, including the failure to estimate distances and the difficulty of accurately recognizing points in fog or haze, it can perform well in waste management. Therefore, this study proposes an Intelligent Garbage Monitoring Scheme based on an improved YOLOv3 Target Detection Algorithm (IGMS-iYTDA) to classify the IoT’sgarbages (IoT) enabled trash can. The performance of the proposed scheme has been evaluated and illustrated for various classification evaluation metrics. The evaluation results show the highest classification accuracy of 99.9% compared to existing models for the proposed scheme.

A facial recognition device is a technology that can be used for authenticating a human face by ID verification and for calculating facial features from a single image using a visual image or a video to a facial database. Computer vision’s main objective enables identifying images, detecting speech, the concentration of attention, facial expressions, and recognition of emotions. Face identification is an important part of the understanding of audiovisual speech. In this paper, Intelligent Schemes for human-computer interaction (IS-HCI) provides a broader and more expressive range of computer-based vision features for processing data from one or more sensors. In particular cases of impairment, alternative interaction methods dependent on computer vision can be effectively modified. Facial identification is a biometric safety category. This paper aims to help technology-based concepts, allowing a child with a debilitating neuromuscular disorder to communicate with the robot by recognizing facial expressions. The proposed model was suggested to test in photographs taken from children’s videos, and the preliminary findings show that computational interactions with children through facial expression would break down barriers to their relationship with machines with people with limited mobility. Thus the experimental results show the proposed method IS-HCI to enhance facial expression and effectively interact with the children to achieve sensitivity (94.12%), specificity (96.84%), recognition rate (94.23%), probability (21.78%), accuracy (96.69%), expression prediction rate (98.14%), muscle activity level (79.84%) compared to other methods.

Peer-to-peer electricity transaction is predicted to play a substantial role in research into future power infrastructures as energy consumption in intelligent microgrids increases. However, the on-demand usage of Energy is a major issue for families to obtain the best cost. This article provides a machine learning predictive power trading framework for supporting distributed power resources in real-time, day-to-day monitoring, and generating schedules. Furthermore, the energy optimization algorithm used in machine learning (EOA-ML) is proposed in this article. The machine learning-based platform suggested two modules: fuel trading and intelligent contracts based on machine learning implemented predictive analytical components. The Blockchain module enables peers to track energy use in real-time, manage electricity trading, model rewards, and irreversible transaction records of electricity trading. A predictive analysis component based on previous power usage data is designed to anticipate short-term energy usage in the Intelligent Contracts. This study utilizes data from the provincial Jeju, Korea’s electricity department on true energy utilization. This study seeks to establish optimal electricity flow and crowdsourced, promoting electricity between consumers and prosumers. Power trading relies on day-to-day, practical environmental control and the planning of decentralized power capitals to satisfy the demands of smart grids. Furthermore, it employs data mining technologies to obtain and study time-series research from the past electricity utilization data. Thus, the time series analytics promotes power controllingto better future efficient planning and managingelectricity supplies. It utilized numerous statistical methods to assess the effectiveness of the suggested prediction model, mean square error in different models of machine learning, recurring neural networks. The efficacy of the proposed system regarding the delay, throughput, and resource using hyperleader caliper is shown. Finally, the suggested approach is successfully applied for power crowdsourcing between prosumer and customer to reach service reliability based on trial findings. The actual and predicted cost analysis has been increased (95%). It minimizes the delay rate to (40.3%) by improving the efficiency rate.

A Smart Hybrid Home (SHH) system studies power concerns for individual clients with an off-grid electricity connection are studied in a Smart Hybrid Home (SHH) system. The creation of autonomous hybrid electricity systems has been regarded as a way to improve energy independence. The primary objective is to ensure efficient management of energy. Detailed modeling is proposed in which the solar energy component is regarded as the significant source to achieve this aim. A multi-agent energy management system (MA-EMS) is presented in this article. It also includes the storing devices for power (Fuel Cell/Capacitors) for safe power delivery. The technical performance of the decentralised solution is in line with the existing central solution, which offers improved financially and operationally for the implementation and operation of the autonomous method. Furthermore, an IT-management system is created and described to guarantee that the system functions correctly utilizing a multi-agent structure. The proposed electricity management system is meant to recognize multi-agent jobs and comprehend probable situations based on energy attributes and requirements. The findings showed that the method suggested meets the aims of an operational database taken from different climatic services using the Matlab/Simulink Application set for the Smart Electricity Management Method. By implementing the proposed technique the power consumption is increased to 410kw, and it improves the efficiency rate to 88.4%.

Supply chain management is implementing the standards of Industry 4.0 in order to enhance the trust and safety level in the system with invention of the latest technologies. At the same time, the customers are now more focused towards the product quality. Supply chain management is the demand of the era. However, it is difficult to trace and maintain the track data throughout the process during the supply of a product. The traditional solutions were based on centralized third party solutions, where the data is inclined to various security attacks and high risk of tempering. In this paper, a solution for the management of supply of a product has been proposed which is based on IoT with an amalgamation of blockchain so as to increase the transparency of the flow during the supply of the product. The solution will help to enhance the trust as the data will be shared among all participating nodes of the network in blockchain. It also makes instant traceability of the product and this can be done by both the end of the chain. Moreover, this solution will help the consumer to check whether the product is from a reliable source or not. For the implementation and testing of smart contracts the authors have used Remix IDE, MetaMask and RinkeyBy.

In the economic growth of the industry, the marine economic prediction management system occupies a significant position. The Marine Forecast Management System is of considerable importance to help establish rules and clues on the potential progress of the Marine Forecast Management System. Many investigations into the risk of the maritime supply chain currently concentrate on risk assessment and control and seldom undertake a detailed study of each factor’s reasoning structure. It is difficult to monitor and regulate the globalization of supply chains. Blockchain technology promises to provide accountability, traceability, and safety certain global supply chain management concerns as a distributed digital technology leader. This article critically analyzes blockchain technologies and intelligent contracts with future supply chain management applications. The interpretation framework model uses the Supply Chain Management based on Marine Economic Development (SCM-MED) methods to analyze the maritime supply chain risk mechanism. The model examines the structure and processes of the marine supply chain risk mechanism, offers a scientific framework for controlling the risk of the marine supply chain, and makes corresponding proposals for reducing and monitoring marine supply chain risk. It aims to test this belief by strictly calculating the exchanging value of information and comparing this improved value in the supply chain to reduce lead times and increase the pace of distribution by decreasing load sizes. The experimental result recommended SCM-MED improves the marine economic development ratio (98.2%) and total losses per year (25%).

Coconut is a multipurpose fruit with high economic value and since it is unique to the landscape of Kerala, it plays an important role in the economy of the state. Skilled labour is one of the key components in coconut farming and lack of its availability can hurt its business. Even if this requirement is met, currently practiced traditional methods for plucking the fruit requires the labour to climb the tree which involves a huge risk factor given the height of the tree they have to scale. There are tools that assist in the climb but they can only reduce the risk factor by a small margin. Robotic harvesting is one of the key solutions to the aforementioned problem as it has the ability to perform accurate coconut plucking since it relies on cutting edge object detection modules, it can provide deep insights into the quality of coconuts to be yielded and also excel at working in remote conditions. The primary aim of this paper is to cover the development of a fast as well as accurate perception module for detection of coconuts, which will serve as a strong foundation for any robotic implementation. In this study we try to explore and compare multiple deep learning based object detection frameworks such as Single Shot Detector and YOLO for efficient and accurate deployment on various edge devices like Raspberry Pi and Nvidia jetson nano by using state of the art methods such as quantization aware training, inference accelerators, multiple augmentation strategies (cutmix, mosaic) for best results. We have also curated a novel, manually annotated dataset of drone based coconut videos (effective/usable content of 30 minutes) in order to capture the naturally setting of coconuts i.e. the true distribution of image data containing background noises, occlusion, shadow as well as natural lighting conditions. The peak performance achieved in our study was a frame rate of 12.7 with a mean average precision of 0.4 by using a tiny YOLOv4 on an Nvidia Jetson Nano.

Wireless sensor networks (WSN) allude to gathering of spatially fragmented and committed sensors for observing and documenting various physical and climatic variables like temperature, moistness and, so on. WSN is quickly growing its work in different fields like clinical, enterprises, climate following and so on. However, the sensor nodes have restricted battery life and substitution or re-energizing of these batteries in the sensor nodes is exceptionally troublesome for the most parts. Energy effectiveness is the significant worry in the remote sensor networks as it is significant for keeping up its activity. In this paper, clustering algorithms based on sensor module energy states to strengthen the network longevity of wireless sensor networks is proposed (i.e. modified MPCT algorithm) in which cluster head determination depends on the every cluster power centroid as well as power of the sensor nodes. Correspondence between cluster leader and sink module employ a parameter distance edge for lessening energy utilization. The outcome got shows a normal increment of 60% in network lifetime compared to Low energy adaptive protocol, Energy efficient midpoint initialization algorithm (EECPK-means), Park K-means algorithm and Mobility path selection protocol.

Higher education plays an important role in the construction of a new growth engine of China’s economy under the new normal state. If innovation is likened to a new engine of economic growth in China, higher education is equivalent to energy and fuel provided by the engine energy for intellectual support and knowledge support. Based on Denison factor analysis, the panel data of 30 provinces in the east, middle and west of China from 1997 to 2014 was used to conduct the Cognitive computing for decision-making and choice about the impact of higher education level of human resources on China’s economic growth. The results show that the output elasticity of material capital gradually decreases from west to east, while the output effect of higher education increases gradually from west to east.

A worm is a standalone program, which is self-replicating malware that distributes itself to other computers and networks. An Internet worm can spread across the network and infect millions of computers in truly little time and the damages caused from such attacks are considered extremely high. In addition, these worms also affect the network packet and its performance, where the packets are analyzed by the signature-based intrusion detection system (IDS) and the network performance is analyzed by the NetFlow based IDS. Hence, this article proposes a joint detection of both the signature based and NetFlow based Internet worms using deep learning convolution neural network (DLCNN) with respect to various attacks and it can also prevent the suspicious actions of attackers (cyber-criminals). Additionally, it provides the security for users’ data maintenance, countermeasures, and controls the spreading of the internet worms. The effectiveness of proposed DLCNN model is evaluated using both packet capture (PCAP) and KDD-CUP-99 datasets. Finally, various quality metrics are employed to disclose the superiority of proposed DLCNN model as compared existing machine learning, and back propagated neural network models.

Clustering is an essential part of data analytics and in Wireless Sensor Networks (WSN). It becomes a problem for causes such as insufficient, unavailable, or compromised data in the face of uncertainties. A solution to tackle the instability of clusters due to missed values has been proposed. The fundamental theory determines whether to incorporate an entity into a group if it is not clear and probable. One of the main issues is identifying requirements for three forms of decision definition, including an entity in a cluster, removing an object from a group, or delaying a decision (defer) to involve or rule out a group. Current studies do not adequately discuss threshold identification and use their fixed values implicitly. This work explores using the game theory-based Possibility Clustering Algorithm for Incomplete Data (PCA-ID) framework to address this problem. In specific, a game theory will be described in which thresholds are determined based on a balance between the groups’ precision and generic characteristics. The points calculated are used to elicit judgments for the grouping of unknown objects. Experimental findings on the deep learning datasets show that the PCA-ID increases the overall quality considerably while maintaining comparable precision levels in competition with similar systems.