The primary objective of this research is to enhance the classification accuracy of motor imagery (MI) electroencephalography signals to improve brain–computer interfaces (BCIs) for communication among individuals with mobility limitations. The main challenge is finding the correct frequency bands, efficient time-frequency representations, and accurately classifying these representations. Various classification models are available but have not achieved high accuracy, which is a key evaluation matrix. This research provides a method that first identifies the best frequency range utilizing several fast-converging optimization approaches. After filtering with the identified band, a continuous wavelet transform was used with complex Morlet to obtain temporal frequency representations, which are effective sources of feature representation for MI tasks. These scalograms were then classified using the advanced deep learning model vision transformer, which is well-known for its ability to extract and select features using the attention mechanism. The proposed technique achieved remarkable accuracy, attaining 97.33% on a widely recognized dataset and 89.89% on another dataset, outperforming comparable research. Integrating modern signal processing and a cutting-edge deep model enhances accuracy, allows for neuroprosthetic device control, and offers up new avenues for research in the BCI arena.

Internet-of-Farming Things (IoFT)-enabled smart agriculture can collect data more reliably and frequently to track the crop’s status and other significant information. Considering that smart agriculture requires working with substantial amounts of sensitive data. In light of this, frequent data processing may threaten the confidentiality and integrity of data and IoFT device privacy. Although numerous privacy-preserving data aggregation methods have been implemented to address these issues, they also have certain security vulnerabilities, such as inadequate data confidentiality, collusion attacks, and malicious data mining attacks. Therefore, we introduce a three-tier architecture-assisted redactable blockchain-based secure data aggregation method with source authentication for the fog-enabled IoFT. This work provides an efficient and secure two-level data aggregation model. The proposed model supports resistance to collusion and malicious data mining threats launched by internal or external attackers. It can also achieve perfect data confidentiality and integrity against a malicious aggregator and an inquisitive control center for an authorized IoFT device. Specifically, the detailed performance analysis and theoretical concrete security proofs demonstrate the practicability and efficiency of the proposed model.

Abstract Lie detection is one of the major challenges that is being faced by the forensic sciences. Identification of lie on the basis of a person's mental behavior is a tedious task. Brain‐computer interface is one such medium which provides a solution to this problem by displaying visual stimuli and recording subject's brain responses. A P300 response is elicited whenever a person comes across a familiar stimuli in a series of rare stimuli. This P300 response is used for the lie detection method. In the proposed concealed information test, acquired signals are preprocessed to discard noise. Then, short‐time Fourier transform method is applied to extract features from the preprocessed electroencephalogram signals. To avoid the curse of dimensionality and to reduce computational overhead, binary bat algorithm is applied, which helps in choosing optimal subset of features. The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10‐fold cross‐validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast with most of the state‐of‐the‐art lie detection methods.

Abstract Credit score classification is a prominent research problem in the banking or financial industry, and its predictive performance is responsible for the profitability of financial industry. This paper addresses how Spiking Extreme Learning Machine (SELM) can be effectively used for credit score classification. A novel spike‐generating function is proposed in Leaky Nonlinear Integrate and Fire Model (LNIF). Its interspike period is computed and utilized in the extreme learning machine (ELM) for credit score classification. The proposed model is named as SELM and is validated on five real‐world credit scoring datasets namely: Australian, German‐categorical, German‐numerical, Japanese, and Bankruptcy. Further, results obtained by SELM are compared with back propagation, probabilistic neural network, ELM, voting‐based Q ‐generalized extreme learning machine, Radial basis neural network and ELM with some existing spiking neuron models in terms of classification accuracy, Area under curve (AUC), H ‐measure and computational time. From the experimental results, it has been noticed that improvement in accuracy and execution time for the proposed SELM is highly statistically important for all aforementioned credit scoring datasets. Thus, integrating a biological spiking function with ELM makes it more efficient for categorization.

The advancement of cloud computing has great impact on the medical sector. Due to its storage facility, e-healthcare has emerged as a promising healthcare solution for providing fast and immediate treatment to patients. The PHRs collected and outsourced in the cloud leads to security concern. The data outsourced in the cloud is no more under the direct control of the patient, hence data should be encrypted prior its storage. Existing works based on group signature require high amount of computation. Other issues like confidentiality of private data, efficient key distribution, scalable and flexible fine-grained data access, revocation and tracing the malicious user is yet to be addressed to maintain the integrity of the patients. In this manuscript, we propose EPOC-1-based multi authority CP-ABE which can trace and revoke the malicious user who leaks the real identity and confidential data of the patient without any storage overhead. This methodology of white-box traceability presented in this manuscript, traces the malicious user efficiently. The proposed scheme is validated with some existing policies and makes the healthcare domain more securable under the cloud setup.

Abstract Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.

In the era of data, it is a challenging task to classify continuous data such as electroencephalographic data. The electroencephalographic signal maps several thoughts going in an individual's brain by connecting a device to the human brain. In this paper, we have proposed a deceit identification system using a test called “concealed information test.” The electroencephalographic data have been recorded when the concealed information test is performed for experimental analysis. To enhance the performance of the deceit identification system, the optimization of support vector machine (SVM) parameters and the selection of the EEG channels are performed. This paper implements a binary version of the BAT algorithm (binary BAT algorithm) and the conventional BAT algorithm on the electroencephalography (EEG) data. A novel cost function is also proposed, which utilizes the results of continuous BAT and binary BAT to enhance the system performance. In this synergistic approach, BAT is used for the SVM parameters optimization, and the binary BAT algorithm is applied for the EEG channel selection. The performance of the system is improved, and it is inferred that the channels placed at the occipital lobe of the brain consist of the artifacts. After removing the channels placed on the occipital lobe, i.e., O1, Oz, and O2, and using the optimized SVM parameters, the system's average accuracy increases from 94.11% to 96.8%.