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Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing., Competing Interests: Declaration of competing interest Authors intend no competing interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields. [ABSTRACT FROM AUTHOR]

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. However, IoT devices are highly prone to botnet attacks. To mitigate this threat, a lightweight and anomaly-based detection mechanism that can create profiles for malicious and normal actions on IoT networks could be developed. Additionally, the massive volume of data generated by IoT gadgets could be analyzed by machine learning (ML) methods. Recently, several deep learning (DL)-related mechanisms have been modeled to detect attacks on the IoT. This article designs a botnet detection model using the barnacles mating optimizer with machine learning (BND-BMOML) for the IoT environment. The presented BND-BMOML model focuses on the identification and recognition of botnets in the IoT environment. To accomplish this, the BND-BMOML model initially follows a data standardization approach. In the presented BND-BMOML model, the BMO algorithm is employed to select a useful set of features. For botnet detection, the BND-BMOML model in this study employs an Elman neural network (ENN) model. Finally, the presented BND-BMOML model uses a chicken swarm optimization (CSO) algorithm for the parameter tuning process, demonstrating the novelty of the work. The BND-BMOML method was experimentally validated using a benchmark dataset and the outcomes indicated significant improvements in performance over existing methods. [ABSTRACT FROM AUTHOR]

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models. [ABSTRACT FROM AUTHOR]

The internet of things (ransomware refers to a type of malware) is the concept of connecting devices and objects of all types on the internet. IoT cybersecurity is the task of protecting ecosystems and IoT gadgets from cyber threats. Currently, ransomware is a serious threat challenging the computing environment, which needs instant attention to avoid moral and financial blackmail. Thus, there comes a real need for a novel technique that can identify and stop this kind of attack. Several earlier detection techniques followed a dynamic analysis method including a complex process. However, this analysis takes a long period of time for processing and analysis, during which the malicious payload is often sent. This study presents a new model of dwarf mongoose optimization with machine-learning-driven ransomware detection (DWOML-RWD). The presented DWOML-RWD model was mainly developed for the recognition and classification of goodware/ransomware. In the presented DWOML-RWD technique, the feature selection process is initially carried out using an enhanced krill herd optimization (EKHO) algorithm by the use of dynamic oppositional-based learning (QOBL). For ransomware detection, DWO with an extreme learning machine (ELM) classifier can be utilized. The design of the DWO algorithm aids in the optimal parameter selection of the ELM model. The experimental validation of the DWOML-RWD method can be examined on a benchmark dataset. The experimental results highlight the superiority of the DWOML-RWD model over other approaches. [ABSTRACT FROM AUTHOR]

Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models. [ABSTRACT FROM AUTHOR]

Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, local outlier detection algorithms for data streams are still deficient and better algorithms need to be developed that can effectively analyze the high velocity of data streams to detect local outliers. This paper presents a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on LOF algorithms. It collects and categorizes existing local outlier detection algorithms and analyzes their characteristics. Furthermore, the paper discusses the advantages and limitations of those algorithms and proposes several promising directions for developing improved local outlier detection methods for data streams. [ABSTRACT FROM AUTHOR]

DNA-binding protein (DBPs) has many crucial functions like DNA transcription, recombination, and replication. DBPs are highly associated with AIDS/HIV, cancer, and asthma, while other types of DBPs are the active ingredients in the designing of anti-inflammatory, steroids, and antibiotics. Many approaches were introduced for DBPs detection. It is still highly desirable to establish predictor with high precision. This research encodes different views of features by several PSSM-based feature descriptors including EDF-PSSM (evolutionary difference formula-based position-specific scoring matrix), FPSSM (Filtered position-specific scoring matrix), PSSM-MAB (position-specific scoring matrix-based multiple average blocks), KSB-PSSM (k-separated bigram position-specific scoring matrix), PSSM-AAC (position-specific scoring matrix-amino acid composition), RPSSM (Reduced position-specific scoring matrix), and PSSM-DPC (position-specific scoring matrix-dipeptide composition). The noisy and redundant features were eliminated by two compression techniques named DWT (discrete wavelet transform) and DCT (discrete cosine transform) The obtained features were concatenated and provided to XGBoost (eXtreme Gradient Boosting) and ERT (Extremely Randomized Trees) classifiers for training the model. The XGBoost-based model (iDBP-PMD) acquired the highest 2.72% and 1.24% accuracies on the training and testing datasets, respectively. The results confirmed the superiority of current study over previous approaches. • Designed a novel predictor, iDBP-PMD for discrimination of DNA-binding proteins. • The local & global discriminative information was discovered by PSSM-based different feature methods. • XGBoost was used for training model and prediction. • Our novel predictor achieved the highest success rate. [ABSTRACT FROM AUTHOR]