Optimizing machine learning (ML) model performance relies heavily on appropriate data preprocessing techniques. Despite the widespread use of standardization and normalization, empirical comparisons across different models, dataset sizes, and domains remain sparse. This study bridges this gap by evaluating five machine learning algorithms- Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost)-on datasets of varying sizes from the business, health, and agriculture domains. This study assessed the models without scaling, with standardized data, and with normalized data. The comparative analysis reveals that while standardization consistently improves the performance of linear models like SVM and LR for large and medium datasets, normalization enhances the performance of linear models for small datasets. Moreover, this study employs SHapley Additive exPlanations (SHAP) summary plots to understand how each feature contributes to the model’s performance interpretability with unscaled and scaled datasets. This study provides practical guidelines for selecting appropriate scaling techniques based on the characteristics of datasets and compatibility with various algorithms. Finally, this investigation laid the foundation for data preprocessing and feature engineering across diverse models and domains which offers actionable insights for practitioners.

Imbalanced datasets pose a persistent challenge in business data mining, particularly in high-stakes domains such as financial risk prediction and customer

Unlike SC-FDMA (single carrier-frequency division multiple access), just combining DFT (Discrete Fourier Transform) spreading and FBMC-OQAM (filter bank multicarrier with offset quadrature amplitude modulation) results in only marginal PAPR (peak to average power ratio) reduction. To utilize the single carrier effect of DFT spreading, a special condition of the coefficients at each subcarrier’s IQ (In-phase and Quadrature-phase) channels should be satisfied. As a starting point, we first derive this condition which we call the ITSM (identically-time-shifted-multicarrier) condition. Then, based on this condition, we propose a new type of FBMC for low PAPR. The main features of the proposed scheme are summarized as follows. Firstly, in order to further enhance the amount of PAPR reduction, we generate the four candidate versions of the DFT-spread and ITSM-conditioned FBMC waveform and select the one with minimum peak power. Even with multiple candidate generation, the major computation parts such as DFT and IDFT are shared and need to be performed only once, unlike the conventional SI (side information)-based PAPR reduction schemes. Consequently, with a fractional complexity overhead compared to the previous DFT-spread FBMC, the proposed scheme achieves a PAPR reduction comparable to that of SCFDMA. Secondly, the proposed scheme transmits only two bit SI per data block consisting of multiple FBMC-OQAM symbols. Hence, the SI overhead is significantly low compared to the usual SI-based schemes such as SLM (selective mapping) or PTS (partial transmit sequence).

As a promising technique, power beacon provides ability of wireless energy transfer to relaying devices in Internet of Things (IoT) to serve far devices with respect to low latency and high spectrum efficiency manner. Mobile edge computing (MEC) enables smart devices to offload parts of their computation-workloads to the cellular base stations associated with edge servers. This paper introduces a promising multiple access technique, namely non-orthogonal multiple access (NOMA) to provide communication service between the base station associated with MEC and far IoT devices. In this paper, we indicate that the multiple power beacons (PBs) benefits to uplink of NOMA-MEC system with the wireless powered IoT. More antennas equipped at smart device and more PBs exhibit reasonable outage performance for the considered system. The simulation results reveal in two folds: (1) our proposed scheme can simultaneously achieve higher spectrum efficiency and ensure prolong lifetime of IoT devices; (2) the mobility of relay leads to select PB appropriately and hence the suitable scheme is realized as comparing two schemes related to ability of energy harvesting and acceptable outage behavior.

The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.

Among the prevalent cyberattacks on Android devices, a ransomware attack is the most common and damaging. Although there are many solutions for detecting Android ransomware attacks, existing solutions have limited detection accuracy and high computational complexity. This paper proposes a new Android ransomware detection method based on traffic analysis to address the limitations. We exploit particle swarm optimization (PSO) to select traffic characteristics. Then, based on the selected traffic features, we classify the data traffic using decision tree and random forest classifiers. We examine ransomware cyberattacks in two distinct circumstances. In the first case, we find ransomware traffic; in the second, we locate a specific form of malware traffic among benign traffic. The proposed PSO-assisted feature selection enables the classifier to improve the detection accuracy significantly. The random forest is found to achieve the highest performance in detecting ransomware, whereas the decision tree is the best for detecting the types of ransomware. The accuracy improvements are 2.26% and 3.7% in the first and second scenarios, respectively. The proposed method removes 56.01% to 91.95% of the features. The proposed method convergences quickly, as the optimization reaches an optimum value of about ten iterations.