Web Intelligence

The on-demand transport (ODT) systems have developed worldwide as they have significant social, environmental, and economic benefits. Even with those benefits, it’s still important to gain popular acceptance. The acceptance key is the reactivity of the system in providing fast and reliable solutions whilst respecting vehicles’ and clients’ constraints. This paper presents a decentralized multi-agent approach to model and solve the ODT problem in a static road network. The agents interact with each other using the A* algorithm to find an optimal solution for each transport demand. The optimal solution is expressed by the fastest trajectory taken by the cheapest vehicles. We utilize factual data from a Lebanese city to do experiments evaluating the proposed approach.

A capability of cloud-based IDS in identifying complicated and anonymous attacks is rising in the current era. However, unwanted delays hinder the detection rate. A malicious user might utilize vast quantities of computational power. The cloud provides to perform attacks both within and without the cloud. Furthermore, there are major challenges for intrusion detection due to the ease of the cloud and also the continual restructuring and movement of cloud resources. Intruder detection, feature extraction, and data processing are all included in the novel optimization-based Intrusion Detection System (IDS) paradigm that will be presented in this study. Data normalization is used to first pre-process the input data. Then, appropriate feature extraction is carried out, including the extraction of (a) raw features, (b) statistical features, then (c) higher-order statistical features using suggested kurtosis. The detection phase is then applied to the retrieved features. A two-stage ensemble method is suggested for finding intruders in clouds. Random forest (RF), Support Vector Machine (SVM), optimal Neural Network (NN), and RNN make up the suggested ensemble technique. The RF, SVM, and Optimized NN algorithms are directly fed the collected features. The output of these classifiers is then provided to the RNN classifier (i.e.), RF output to RNN1, SVM output to RNN2, and optimized NN output to RNN3. Then, the weighted average of RNN 1, 2, and 3 is considered as the final output. A Self Adaptive Salp Swarm Optimization optimizes the weights of NN for exact detection (SA-SSO). Finally, a test is conducted to confirm the developed model’s superiority.

Lung cancer is the most lethal and severe illness in existence. However, lung cancer patients may live longer if they receive early detection and treatment. In the medical field, the best imaging technique is CT scan imaging as it is more complex for doctors to identify cancer and interpret from CT scan images. Consequently, the computer-aided diagnosis (CAD) is more useful for doctors to find out cancerous nodules. To identify lung cancer, a number of CAD techniques utilising machine learning (ML) and image processing are used nowadays. The goal of this study is to present a novel method for detecting lung cancer that entails four main steps: (i) Pre-processing, (ii) Segmentation, (iii) Feature extraction, and (iv) Classification. ”The input image is first put through a pre-processing step in which the CLAHE model is used to pre-process the image. The segmentation phase of the pre-processed images is then initiated, and it makes use of a modified Level set segmentation method. The retrieved features from the segmented images include statistical features, colour features, and texture features (GLCM, GLRM, and LBP). The Layer Fused Conventional Neural Network (LF-CNN) is then utilised to classify these features in the end. Particularly, layer-wise modification is carried out, and along with that, the LF-CNN is trained by the Modified Cat swarm Optimization (MCSO) Algorithm via selecting optimal weights. The accepted scheme is then compared to the current models in terms of several metrics, including recall, FNR, MCC, FDR, Threat score, FPR, precision, FOR, accuracy, specificity, NPV, FMS, and sensitivity.

One of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.

The fast advancement of information technology has resulted in more efficient information storage and retrieval. As a result, most organizations, businesses, and governments are releasing and exchanging a large amount of micro data among themselves for commercial or research purposes. However, incorrect data exchange will result in privacy breaches. Many methods and strategies have been developed to address privacy breaches, and Anonymization is one of them that many companies use. In order to perform anonymization, identification of the Quasi Identifier (QI) is significant. Hence this paper proposes a method called Quasi Identification Based on Tree (QIBT) for automatic QI identification. The proposed method derives the QI, based on the relationship between the numbers of distinct values assumed by the set of attributes. So, it uses the tree data structure to derive the unique and infrequent attribute values from the entire dataset with less computational cost. The proposed method consists of four phases: (i) Unique attribute value computation (ii) Tree construction and (iii) Computation of quasi-identifier from the tree (iv) Applying Anonymization Technique to the identified QI. Attributes with high risk of disclosure are identified using our proposed algorithm. Synthetic data are created exclusively for the detected QI using a partial synthetic data generating technique to improve usefulness. The suggested method’s efficiency is tested with a subset of the UCI machine learning dataset and produces superior results when compared to other current approaches.

In order to improve the effect of fault location, this paper proposes an accurate fault location method for wireless sensor networks based on random matrix theory. The standard non Hermite matrix is used to extract accurate fault location data. Considering the volatility of the original data, the original random matrix is preprocessed. Based on the real-time sliding time window method, the space-time characteristic data of network faults are determined, and the precise fault location of wireless sensor networks based on random matrix theory is realized.Experimental results show that the false positive rate of the proposed method is only 2%. The average fault location accuracy is as high as 96.4% and the fault location time is only 15.1 s, which shows that the proposed method has a good location effect.