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Android malware has been rising alongside the popularity of the Android operating system. Attackers are developing malicious malware that undermines the ability of malware detecting systems and circumvents such systems by obfuscating their disposition. Several machine learning and deep learning techniques have been proposed to retaliate to such problems; nevertheless, they demand high computational power and are not energy efficient. Hence, this article presents an approach to distinguish between benign and malicious malware, which is robust, cost-efficient, and energy-saving by characterizing CNN-based architectures such as the traditional CNN, AlexNet, ResNet, and LeNet-5 and using transfer learning to determine the most efficient framework. The OAT (of-ahead time) files created during the installation of an application on Android are examined and transformed into images to train the datasets. The Hilbert space-filling curve is then used to transfer instructions into pixel locations of the 2-D image. To determine the most ideal model, we have performed several experiments on Android applications containing several benign and malicious samples. We used distinct datasets to test the performance of the models against distinct study questions. We have compared the performance of the aforementioned CNN-based architectures and found that the transfer learning model was the most efficacious and computationally inexpensive one. The proposed framework when used with a transfer learning approach provides better results in comparison to other state-of-the-art techniques.

With the extensive use of Android applications, malware growth has been increasing drastically. The high popularity of Android devices has motivated malware developers to attack these devices. In recent times, most researchers and scholars have used deep learning approaches to detect Android malware. Although deep learning techniques provide good accuracy and efficiency, they require high computational cost to train huge and complex data sets. Hence, there is a need for an approach that can efficiently detect novel malware variants with a minimum computational cost. This paper proposes a novel framework for detecting and clustering Android malware using the transfer learning and the topic modelling approach. The transfer learning approach minimizes new training data by transferring well-known features from a qualified source model to a destination model, and hence, a high amount of computational power is not required. In addition, the proposed framework clusters the detected malware variants into their corresponding families with the help of Latent Dirichlet Allocation and hierarchical clustering techniques. For performance assessment, we performed several experiments with more than 50K Android application samples. In addition, we compared the performance of our framework with that of similar existing traditional machine learning and deep learning models. The proposed framework provides better accuracy of 98.3% during the classification stage by using the transfer learning approach as compared to other state-of-the-art Android malware detection techniques. The high precision value of 98.7% is obtained during the clustering stage while grouping the obtained malicious applications into their corresponding malware families.

Imbalance in dataset enforces numerous challenges to implement data analytic in all existing real world applications using machine learning. Data imbalance occurs when sample size from a class is very small or large then another class. Performance of predicted models is greatly affected when dataset is highly imbalanced and sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning technique combat with imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classify imbalanced dataset and their application areas.

Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original imagery's complete dynamic range. The study explores various noise reduction methods, such as minimum noise fraction (MNF), principal component analysis (PCA), wavelet denoising, non-local means (NLM), and denoising autoencoders, aimed at enhancing the signal-to-noise ratio. The effectiveness of these techniques is evaluated through visual quality, mean square error (MSE), and peak signal-to-noise ratio (PSNR), alongside their impact on mineral exploration. Furthermore, the paper investigates the application of machine learning algorithms on denoised data for mineral identification, highlighting the potential of integrating denoising techniques with machine learning for improved mineral exploration. This comparative analysis aims to identify the most efficient noise removal methods for hyperspectral imagery, facilitating higher quality data for subsequent analysis.

Machine learning is a branch of computer science concerned with developing algorithms & models capable of 'learning through data and iterations'. Deep learning simulates the structure and function of human organs and diseases using artificial neural networks with more than one hidden layer. The primary purpose of this work is to develop and test computer vision and machine learning algorithms for classifying Huanglongbing (HLB)-infected, healthy, and unhealthy leaves and fruits of the citrus plant. The images were segmented using a normalized graph cut, and texture information was extracted using a co-occurrence matrix. The collected attributes were used for classification and support vector machine (SVM), and deep learning methods were employed. When rating the classification outcomes, the accuracy of the classification and the number of false positives and false negatives were considered. The result shows that Deep Learning could create categories up to 96.8% of HLB-infected leaves and fruits. Despite a broad variance in intensity from leaves collected in North India, this method suggests it could be beneficial in diagnosing HLB.

Banks play an important role in a nation's economy, benefiting both residents and governments. In the recent past, many fraudulent happenings in banks have been identified as a result of vested interests. This paper examines the most common forms of insider fraud that occur in banks and attempts to categorise them. We include a description, factors associated to certain categories of fraud, and challenges in detecting frauds in this article. It is critical to identify such fraudulent events automatically until it is too late and get the individuals or groups of individuals into agreement. The process mining techniques is useful because it aids in the detection of irregular data.

Citrus disease has a significant influence on agricultural productivity these days, so technology based on artificial intelligence has been developed for creating computer vision (CV) models. By spotting disease in its early stages and enabling necessary productivity actions, CV in agriculture improves the production of agricultural goods. In this paper, we developed a CV-based citrus disease detection model called the self-attention dilated convolutional neural network optimized restricted Boltzmann machine (SADCNN-ORBM) model, which consists of two crucial parts: a SADCNN for disease segmentation and an ORBM optimized by the self-adaptive coati optimization (SACO) algorithm to improve the classification performance of diseases, which successfully divides the disease type into three groups: anthracnose, melanose, and brown spot. Numerous feature sets, such as texture features, three-channel red, green, blue (RGB) features, local binary pattern (LBP) features, and speeded-up robust features (SURF) features, are combined and given as input into the classification layer in the proposed model. We compare our proposed model's performance with existing methods by using several evaluation metrics. The findings demonstrate the SADCNN-ORBM model's superiority in precisely recognizing and classifying citrus illnesses, outperforming all available techniques.

Agricultural production is essential to the economic development of any country. That's why disease identification in plants is critical in the agricultural sector, as a disease in plants is a regular occurrence. If reasonable precautions are not taken on time, plants can significantly affect the environment, affecting product quality, quantity, and productivity. Lemons, grapes, limes, oranges, etc., are common citrus fruits grown the entire world. About 50% of citrus fruits are wasted each year because of diverse plant sicknesses. This paper offers a survey of various approaches for detecting and classifying diseases in citrus plant leaves. A comprehensive taxonomy of citrus leaf diseases is also presented. A study of automatic illness recognition and classification methods are also discussed. We explore different methods for pre-processing, segmentation, extraction of features and grouping. Discuss also the relevance of functional extraction and techniques of deep learning.

The amount of data available and information over the past few decades has grown manifold and will only increase exponentially. The ability to harvest and manipulate information from this data has become a crucial activity for effective and faster development. Multiple algorithms and approaches have been developed in order to harvest information from this data. These algorithms have different approaches and therefore result in varied outputs in terms of performance and interpretation. Due to their functionality, different algorithms perform differently on different datasets. In order to compare the effectiveness of these algorithms, they are run on different datasets under a given set of fixed restrictions (e.g., hardware platform, etc.). This paper is an in-depth analysis of different algorithms based on trivial classifier algorithm, kNN, and the newly developed ARSkNN. The algorithms were executed on three different datasets, and analysis was done by evaluating their performance taking into consideration the accuracy percentage and execution time as performance measures.

Accurately estimating the software size, cost, effort and schedule is probably the biggest challenge facing software developers today. It has major implications for the management of software development because both the overestimates and underestimates have direct impact for causing damage to software companies. Lot of models have been proposed over the years by various researchers for carrying out effort estimations. Also some of the studies for early stage effort estimations suggest the importance of early estimations. New paradigms offer alternatives to estimate the software development effort, in particular the Computational Intelligence (CI) that exploits mechanisms of interaction between humans and processes domain knowledge with the intention of building intelligent systems (IS). Among IS, Artificial Neural Network and Fuzzy Logic are the two most popular soft computing techniques for software development effort estimation. In this paper neural network models and Mamdani FIS model have been used to predict the early stage effort estimations using the student dataset. It has been found that Mamdani FIS was able to predict the early stage efforts more efficiently in comparison to the neural network models based models.