Interactive IoT Cloud Deep Learning Model for Research Development in Universities for the Educational Think Tank

The construction of university education think tanks using the interactive service platform enables the sharing of research resources, encourages cross-disciplinary research collaboration, and fosters innovation in education. It also helps to build a stronger relationship between academia and industry by enabling practitioners to participate in research activities. The Internet of Things (IoT) can be used to collect and analyze data from various sources, including sensors and other connected devices, to provide insights into education-related issues. The integration of these technologies in university education thinks tanks can help to enhance the efficiency and effectiveness of research, decision-making, and collaboration processes. Hence, this paper constructed an Interactive IoT Cloud Computing Platform (IIoTCC). With IIoTCC model the innovative idea about research and other ideas are collected and stored in a Cloud environment. Within the environment, information collected is stored in the stacked architecture model with the voting-based model. Through the stacked model, information is processed and evaluated for academic activities. The IoT environment is implemented through IIoTCC for the information process in a deep learning environment for academic-related issues. Simulation analysis expressed that IIoTCC model achieves a higher accuracy rate of 99.34% which is significantly higher than conventional classifiers

Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning

Designing assistive technology for getting more independence for blind people when performing everyday tasks: an auditory-based tool as a case study

Everyday activities and tasks should in theory be easily carried by everyone, including the blind. Information and Communication Technology (ICT) has been widely used for supporting solutions. However, the solutions can be problematic for the visually impaired since familiarity with digital devices is often required. Or, indeed the procedure can be perceived as fiddly or impractical particularly for repetitive tasks due to the number/type of steps required to complete the task. This paper introduces a simple audio-based tool aimed at supporting visually-impaired people in the seemingly simple activity of checking whether the light in a room is on or off. It is an example of potential low tech devices that can be designed without the need for specific skills or knowledge by the user, and that functions in a practical way. In this context, we discuss the main issues and considerations for totally blind users in identifying whether a light is switched on. The proposed prototype is based on a simple circuit and a form of auditory feedback which informs the user whether they are switching on or off the light. Two prototypes have been designed and built for two different kinds of installation. For the subsequent second prototype, three different versions are proposed to provide a blind person with further support in easily identifying the light status at home. The new design includes enhanced auditory feedback and modifications to the dimensions. The evaluation conducted by involving various groups of end-users revealed the usefulness of the proposed tool. In addition, a survey conducted with 100 visually-impaired people reported the limitations and difficulties encountered by the blind in using existing devices. Moreover, the study revealed the interest from 94% of the participants for a potential (new) basic tool integrable with the existing lighting system. This study gives a contribution in the ambient intelligence field by (1) showing how an auditory-based tool can be used to support totally blind people to check the lights in an autonomous and relatively simple way; (2) proposing an idea that can be exploited in other application cases that use light feedback; and (3) proposing seven potential recommendations for designing assistive technology tools and common everyday devices, based on information gathered from the online survey

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR

Predicting DDoS Attacks Preventively Using Darknet Time-Series Dataset

The cyber crimes in today’s world have been a major concern for network administrators. The number of DDoS attacks in the last few decades is increasing at the fastest pace. Hackers are attacking the network, small or large with this common attacks named as DDoS. The consequences of this attack are worse as it disrupts the service provider’s trust among its customers. This article employs machine learning methods to estimate short-term consequences on the number and dimension of hosts that an assault may target. KDD Cup 99, CIC IDS 2017 and CIC Darknet 2020 datasets are used for building a prediction model. The feature selection for prediction is based on KDD Cup 99 and CIC IDS 2017 dataset; CIC Darknet 2020 dataset is used for prediction of impact of DDoS attack by employing LSTM (Long Short Term Memory) algorithm. This model can help network administrators to identify and preventively predict the attacks within five minutes of the commencement of the potential attack

A Novel Skin Disease Detection Technique Using Machine Learning

Skin sicknesses present critical medical care difficulties around the world, requiring precise and opportune location for successful therapy. AI became promising stuff for computerizing the discovery and characterization of skin illnesses. This study presents a clever methodology that uses the choice tree strategy for skin sickness location. In computerized location, we utilize an exhaustive dataset containing different skin sickness pictures, including melanoma, psoriasis, dermatitis, and contagious diseases. Dermatologists skillfully mark the dataset, guaranteeing solid ground truth for precise grouping. Preprocessing strategies like resizing, standardization, and quality improvement are applied to set up the symbolism for the choice tree calculation. Then, we remove applicable elements from the preprocessed pictures, enveloping surface, variety, and shape descriptors to catch infection explicit examples successfully. The choice tree model is prepared utilizing these removed elements and the named dataset. Utilizing the choice tree's capacity to learn progressive designs and choice principles, our methodology accomplishes an elevated degree of exactness in grouping skin sicknesses. Extensive experiments and evaluations on a dedicated validation set demonstrate the effectiveness of our decision tree-based method, achieving a classification accuracy of 96%. Our proposed method provides a reliable and automated solution for skin disease detection, with potential applications in clinical settings. By enabling early and accurate diagnoses, our approach has the capacity to improve patient outcomes, trim down healthcare overheads, and alleviate the burden on dermatologists