The construction of an automatic voice pathology detection system employing machine learning algorithms to study voice abnormalities is crucial for the early detection of voice pathologies and identifying the specific type of pathology from which patients suffer. This paper’s primary objective is to construct a deep learning model for accurate speech pathology identification. Manual audio feature extraction was employed as a foundation for the categorization process. Incorporating an additional piece of information, i.e., voice gender, via a two-level classifier model was the most critical aspect of this work. The first level determines whether the audio input is a male or female voice, and the second level determines whether the agent is pathological or healthy. Similar to the bulk of earlier efforts, the current study analyzed the audio signal by focusing solely on a single vowel, such as /a/, and ignoring phrases and other vowels. The analysis was performed on the Saarbruecken Voice Database,. The two-level cascaded model attained an accuracy and F1 score of 88.84% and 87.39%, respectively, which was superior to earlier attempts on the same dataset and provides a steppingstone towards a more precise early diagnosis of voice complications.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.

Devices which are part of the Internet of Things (IoT) have strong connections; they generate and consume data, which necessitates data transfer among various devices. Smart gadgets collect sensitive information, perform critical tasks, make decisions based on indicator information, and connect and interact with one another quickly. Securing this sensitive data is one of the most vital challenges. A Network Intrusion Detection System (IDS) is often used to identify and eliminate malicious packets before they can enter a network. This operation must be done at the fog node because the Internet of Things devices are naturally low-power and do not require significant computational resources. In this same context, we offer a novel intrusion detection model capable of deployment at the fog nodes to detect the undesired traffic towards the IoT devices by leveraging features from the UNSW-NB15 dataset. Before continuing with the training of the models, correlation-based feature extraction is done to weed out the extra information contained within the data. This helps in the development of a model that has a low overall computational load. The Tab transformer model is proposed to perform well on the existing dataset and outperforms the traditional Machine Learning ML models developed as well as the previous efforts made on the same dataset. The Tab transformer model was designed only to be capable of handling continuous data. As a result, the proposed model obtained a performance of 98.35% when it came to classifying normal traffic data from abnormal traffic data. However, the model’s performance for predicting attacks involving multiple classes achieved an accuracy of 97.22%. The problem with imbalanced data appears to cause issues with the performance of the underrepresented classes. However, the evaluation results that were given indicated that the proposed model opened new avenues of research on detecting anomalies in fog nodes.

Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop a smart environment, enhancements to the execution, sustainability, and security of traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions to achieve security in the smart environment. This article introduces intrusion detection using chaotic poor and rich optimization with a deep learning model (IDCPRO-DLM) for ubiquitous and smart atmospheres. The IDCPRO-DLM model follows preprocessing, feature selection, and classification stages. At the initial stage, the Z-score data normalization system is exploited to scale the input data. Additionally, the IDCPRO-DLM method designs a chaotic poor and rich optimization algorithm-based feature selection (CPROA-FS) approach for selecting feature subsets. For intrusion detection, butterfly optimization algorithm (BOA) with a deep sparse autoencoder (DSAE) is used. The simulation analysis of the IDCPRO-DLM technique is studied on the benchmark CICIDS dataset and the comparison results show the better performance of the IDCPRO-DLM algorithm over recent state-of-the-art approaches with a maximum accuracy of 98.53%.

Malware is malicious software (harmful program files) that targets and damage computers, devices, networks, and servers. Many types of malware exist, including worms, viruses, trojan horses, etc. With the increase in technology and devices every day, malware is significantly propagating more and more on a daily basis. The rapid growth in the number of devices and computers and the rise in technology is directly proportional to the number of malicious attacks—most of these attacks target organizations, customers, companies, etc. The main goal of these attacks is to steal critical data and passwords, blackmail, etc. The propagation of this malware may be performed through emails, infected files, connected peripherals such as flash drives and external disks, and malicious websites. Many types of research in artificial intelligence and machine learning fields have recently been released for malware detection. In this research work, we will focus on detecting malware using deep learning. We worked on a dataset that consisted of 8970 malware and 1000 non-malware (benign) executable files. The malware files were divided into five types in the dataset: Locker, Mediyes, Winwebsec, Zeroaccess, and Zbot. Those executable files were pre-processed and converted from raw data into images of size 224 * 224 * 3. This paper proposes a multi-stage architecture consisting of two modified VGG-19 models. The first model objective is to identify whether the input file is malicious or not, while the second model objective is to identify the type of malware if the file is detected as malware by the first model. The two models were trained on 80% of the data and tested on the remaining 20%. The first stage of the VGG-19 model achieved 99% accuracy on the testing set. The second stage using the VGG-19 model was responsible for detecting the type of malware (five different types in our dataset) and achieved an accuracy of 98.2% on the testing set.

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset.