An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.
With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.
Therapeutic plasma exchange (TPE) is an effective and affordable treatment option in most parts of Southeast Asia (SEA). In 2018, the SEA TPE Consortium (SEATPEC) was established, consisting of regional neurologists working to improve outcome of various autoimmune neurological diseases. We proposed an immunotherapeutic guideline prioritizing TPE for this region. We reviewed disease burden, evidence-based treatment options, and major guidelines for common autoimmune neurological disorders seen in SEA. A modified treatment algorithm based on consensus agreement by key-opinion leaders was proposed. Autoimmune antibody diagnostic testing through collaboration with accredited laboratories was established. Choice of first-line immunotherapies (IVIg/corticosteroid/TPE) is based on available evidence, clinicians' experience, contraindications, local availability, and affordability. TPE could be chosen as first-line therapy for GBS, CIDP, MG (acute/short term), IgG, A paraproteinemic neuropathy, and NMDAR encephalitis. Treatment is stopped for acute monophasic conditions such as GBS and ADEM following satisfactory outcome. For chronic immune disorders, a therapy taper or long-term maintenance therapy is recommended depending on the defined clinical state. TPE as second-line treatment is indicated for IVIg or corticosteroids refractory cases of ADEM, NMOSD (acute), MG, and NMDAR/LGI1/CASPR2/Hashimoto's encephalitis. With better diagnosis, treatment initiation with TPE is a sustainable and effective immunotherapy for autoimmune neurological diseases in SEA.