With the shift of focus to cloud computing, there is a major concern regarding the privacy of its users. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. In this paper, a novel ensemble model, Stacked Ensemble-autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models-autoencoder, GRU, and MLP-that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Malware infiltrated at least one device in almost every household. Behind your favourite emoji are hidden the core secrets of your personality.The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. The list of your most commonly used emojis defines a lot about you and your behavioural traits. World Emoji Day is celebrated every 17th of July to acknowledge the difference emojis have bought to written or textual communications. Now, conversations are easier and more exciting than ever. In this article, we'll find out the emojis you tend to use the most based on your zodiac sign along with highlighting the unique qualities which make you amazing and rare, says Sidhharrth S Kumaar, a celebrated Astro numerologist. Represented by the zodiac symbol Ram, this fire sign takes life head-on. They are passionate about everything they love. It seems their biggest love affair is with life itself. However, their bossy nature picks fights quite easily. Though, this doesn't stop them from being the life of the party. Their situations lead them to use these emojis the most - "Face with tears of joy", "angry face", and "person with crown". Who doesn't love a comfortable and luxurious lifestyle? Well, a Taurus can't live without it. Everything from their taste in fashion to aesthetics has to be the best. On the contrary, they are extremely patient and chill. Just don't poke them if you fear your life. They use "smiling face with a halo", ", cocktail glass" or "wine glass" and "bomb" the most. It's almost as if the phrase "Jack of all trades, master of none" (in this case, master of "some") was made for Gemini. Someway these intellectuals happen to know about everything. Also, they gel easily with others as more people equals more information. Emojis like "handshake", "woman/man dancing" and "thinking face" are their usuals. Your lucky stars are shining if you're in contact with a Cancerian. They are the most loyal friends and loving romantic partners. Represented by the crab, they prioritize living in their comfort zone.
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