In today's interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces ...
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In today's interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitationalsearchalgorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies.
In the assessment of the features of strategic bidding choice issues, this paper proposes a novel procedure that optimizes strategic bidding using intelligent gravitational search algorithm (IGSA) for profit maximizat...
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ISBN:
(纸本)9789811502149;9789811502132
In the assessment of the features of strategic bidding choice issues, this paper proposes a novel procedure that optimizes strategic bidding using intelligent gravitational search algorithm (IGSA) for profit maximization of power suppliers in an emerging power market. In this paper, two approaches are suggested. One suggests using the inverse agents in the assessment procedure of GSA. It empowers improved investigation of the exploration space and avoids trapping of the solution in a local optimum result. Another is a new gravity constant control procedure to avoid repetitive calculation and enhance the speed of convergence. The suggested procedure has been tested on the IEEE 30-bus system. The experimental solutions of both result qualities in terms of profit and calculation efficiency demonstrate the efficacy and strength of IGSA to other approaches such as Shuffled Frog Leaping algorithm (SFLA), Particle Swarm Optimization (PSO), Genetic algorithm (GA), and Monte Carlo (MC).
Video piracy is a challenging issue in the modern world. Approximately 90% of newly released films were illegally distributed around the world via the Internet. To overcome this issue, video watermarking is an effecti...
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Video piracy is a challenging issue in the modern world. Approximately 90% of newly released films were illegally distributed around the world via the Internet. To overcome this issue, video watermarking is an effective process that integrates a logo in video frames as a watermark. Therefore, this paper presents an efficient lossless video-watermarking scheme based on optimal keyframe selection using an intelligent gravitational search algorithm in linear wavelet transform. This technique obtains color motion and motionless frames from the cover video by the histogram difference method. One-level linear wavelet transform is performed on the chrominance channel of motion frames and a low-frequency sub-band LL opts for watermark embedding. The performance of the proposed technique has been evaluated against 12 video processing attacks in terms of imperceptibility and robustness. Experiments demonstrate that the proposed technique outperforms five state-of-the-art schemes on the considered attacks.
The histopathological image classification is a vivid application for medical diagnosis and neural network has been successful in the image classification task. However, finding the optimal values of the neural networ...
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ISBN:
(纸本)9783030369873;9783030369866
The histopathological image classification is a vivid application for medical diagnosis and neural network has been successful in the image classification task. However, finding the optimal values of the neural network is still a challenging task. To accomplish the same, this paper considers a two-layer neural network which is optimized through intelligent gravitational search algorithm. Further, the optimized two-layer neural network is applied for the histopathological tissue classification into healthy and inflamed. The proposed method is validated on the publicly available tissue dataset, namely Animal Diagnostic Laboratory (ADL). The experimental results firm the better performance of the proposed method against state-of-the-art methods in terms of seven performance measures, namely recall, specificity, precision, false negative rate (FNR), accuracy, F1-score, and G-mean.
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