Ethically Governed Artificial Intelligence Based Innovative Business Research in Finance and Marketing System

Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security" in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence techniques.Likewise, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint.

A Novel Dynamic Watermarking for Secure Data Protection from Cyber Theft Based on Artificial Intelligence Supervision

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security" in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence techniques.Likewise, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint. 

Medical imaging is extremely important in the domain of medicine. Image classification is now utilized to distinguish aberrant tissues from healthy tissue in brain imaging. The brain tumor is identified from MRI images by using some classification techniques, where the area of the tumor as well as the tumor size is detected. Automatic tumor detection using brain MRI is efficient and time- saving, assisting the neurologists in diagnosis.  Tumors can increase the risk of  cancer,  which  is  the  most  common  cause  of  death  or major cause of mortality worldwide. To detect brain tumors at the moment, effective automation of tumor detection is essential. Marker based Watershed algorithm is a typical segmentation technique which is used for identifying brain tumors. For brain tumor detection, we performed marker based watershed classification on MRI images with the use of gray scale images, then   by noise removal and morphological operations. The steps in the methodology are as follows:  Gray-level and sharpening was used in the pre-processing, and the image was segmented using thresholding as well as the marker based watershed algorithm, and the CNN was used for classifying the images. Finally, the tumor's location and size were determined.

Automated medical image classification using deep learning

Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

/pdf/automated-registration-of-multiangle-sar-images-using-nfrkx2s1.pdf

Automated Registration of Multiangle SAR Images Using Artificial Intelligence

Sentiment analysis Sentiment analysis is the process of extracting information from the text and it is considered as opinion mining. Machine learning experiments have been shown in the study that involves review, blog, for some texts that are written in different languages including Dutch, English and French. Set of example sentence have been set that are manually labeled, neutral, positive or negative. The study covers the curiosity of the consumer regarding the specific consumption products. Categorization models have been developing that has been used in the study. Number of issues that includes noisy nature of the text has been discussed in the study. With an accuracy of roughly 83 percent for English texts, we can determine positive, negative, and neutral sentiments toward the subject under investigation using unigram features augmented with linguistic information. The role of active learning approaches in minimizing the number of instances that need to be manually annotated is discussed in this article. Our studies also give data on the transferability of learned models between domains and languages. Here, Sentiment analysis of a particular person has studied using a K-Means Clustering and SVM classifier to classify the sentiment of a person from the text

The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier

With the evolution of communication systems and their methodologies, the significance of designing various types of antennae has increased to suit the different necessities. While some antennas, especially the substrate-integrated antennas are hard to optimize, the microstrip patch antennas are easy to design and optimize. Besides, providing an economic advantage, it also delivers higher efficiencies in terms of radiation and gain, making it appropriate for communication-based applications. The concerned work proposes a psi-shaped optimized antenna that is capable of scanning and receiving the 2.4 GHz resonant frequency for applications like radio-frequency (RF) energy harvester systems (RFEHS), radio-frequency identification (RFID) and wireless local area network (WLAN) communications. The structure has been tested with an inset feeding technique and the substrate used is arlon with differential conductor and cover layer elements. The entire design and simulation have been carried out in advanced design system (ADS) layout and momentum microwave electromagnetic (EM) mode. The designed psi-shaped antenna is able to produce a maximum gain of 5.47 dBi with a radiation efficiency of 70.39%, making it suitable for the aforesaid applications. A software-based prototype simulation has also been carried out to find the effectiveness of the proposed antenna.

2.4 GHz Operational PSI-Shaped Patch Antenna Design For Multi Purpose Applications

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Automated Registration of Multiangle SAR Images Using Artificial Intelligence

The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier

2.4 GHz Operational PSI-Shaped Patch Antenna Design For Multi Purpose Applications