Enhancing the quality of human's daily life in respects of comfort is the chief objective of smart environments (SE). The Internet of Things (IoT) is basically an increasing network of smart objects. It commences diverse services in human's routine life relying on its available and also dependable activities. The chief problems in any real-globe SE centered upon the IoT model are the security in addition to privacy. The security susceptibility in IoT-centered systems creates security risks that affect SE applications. So, an Intrusion Detection System (IDS) based Modified Adaptive Neuro-Fuzzy Inference System is proposed aimed at detecting the attacks on IoT Smart Cities (SM). The proposed method comprises '2' phases. They are training and testing. First, the IDS are trained by performing three processes: preprocessing, feature selection (FS) and classification. For training, the proposed technique utilizes the data from the NSL_KDD dataset. Then, IoT sensor values are tested employing the same steps of training. The result of testing comprises '2' models. They are the attacked data and non-attacked data. The non-attacked data is sent to the user securely with the help of Improved Rivest Shamir Adleman method. After that, the user receives and decrypts the data. Then, the decrypted data is forecasted for further analysis. The proposed techniques' experimental outcomes used in FS, classification, and also secure data transmission are contrasted with the existent methods. KeywordsAttack detection • Internet of Things (IoT) • Smart cities • Crow search optimization (CSO) • Chaotic mapping (CM) • CM based CSO (CM-CSO) • Adaptive neuro fuzzy inference system (ANFIS) • Modified ANFIS (MANFIS) • Intrusion detection system (IDS) • Rivest Shamir Adleman (RSA) encryption • Improved RSA (IRSA)
In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the optimal discriminating feature set (5 features) and classification using the decision tree constructed with the reduced feature set resulted in better performance. In the second level, grade classification, a fuzzy rule-based approach was used to resolve the uncertainty in discriminating the tumor grades II and III. Membership functions of all grades were defined for all features extracted from brain tumor grade images, to derive the fuzzy inference rules for grade discrimination. Similar to type classification with GA, better grade discrimination performance was exhibited with fuzzy inference rules derived using optimal feature set (13 features) using GA. Overall performance comparison of the proposed bilevel classifier with all features vs GA-based feature selection, shows that evolutionary computing combined with fuzzy rule-based approach is successful in reducing false positives, thereby enhancing classifier performance.
Billions of payment transactions occur in our day-to-day life, but every payment method depends on a material to carry. It is common for users to possess various materials like cash, credit cards and even mobiles to make payment. Meanwhile, it is easy for these materials to be robbed or lost. These instances result in a terrible trauma for the people. This study gives a detailed portrayal of a biometric payment application developed to introduce a concept of material-less payment. It enables the user to make a payment at any location by enrolling their fingertip without possessing any material. It involves a one-time registration of the User details upon all further transactions are validated and processed based on the user's fingerprint where the App takes care of the whole process. This implementation results in a novel payment method and avoids the risk of carrying valuable materials outdoors. This App creates an efficient and safe payment for society.
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