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Dialect recognition System (DRS) is a highly significant subject within the field of speech analysis. The performance of speech recognition systems is adversely impacted by factors such as the age, gender, and dialect features of the speaker. In order to address variations in dialect, it is possible to incorporate DRS into speech recognition systems. The system can be configured to utilize the appropriate speech recognition model based on the identification of the spoken dialect. Currently, there is a lack of available datasets suitable for the development of automatic dialect recognition systems specifically tailored for the Kurdish language. The proposed dataset under consideration is assessed using experimental data that has been gathered by personnel associated with the Computer Science Department at the University of Halabja. As the Kurdish language has three main dialects: Northern Kurdish (Badini variation), Central Kurdish (Sorani variant), and Hawrami, three dialects are included in the dataset.

Skin cancer is a severe problem that is frequently disregarded. In circumstances of manual examination by a clinician, the human eye is occasionally unable to detect disorders precisely from imaging data. Deep learning techniques are increasingly being used nowadays to solve various problems in our daily lives. Therefore, deep neural network techniques are used to create an automated and computerized mechanism for detecting skin illnesses. To identify and diagnose skin illnesses over a range of criteria several neural network algorithms are evaluated and tested in the suggested model to see how well they perform. The networks are constructed to provide better outcomes using the CNN (Convolution neural network) and the Keras Sequential API architectures. The paper also compares the outcomes of the models using several metrics, such as accuracy, precision, f1 score, and recall. The transfer learning model involves seven models like DenseNet201, InseptionResnetV2, MobileNetV2, InceptionV3, ResNet50, DenseNet169, and VGG16. Among the employed models, the DenseNet169 model achieved the highest score of 87.58% in terms of accuracy; also, in terms of sensitivity and F1 score, DenseNet201 achieved the highest scores of 95.28% and 89.09%, respectively. On the other hand, VGG16 gained a score of 89.67% in terms of specificity, and DenseNet169 achieved the highest score of 90.64% in terms of precision.

Presents corrections to the paper, (Corrections to “Deep Learning-Based Intrusion Detection Systems: A Systematic Review”).

The purpose of this paper is to propose a novel combined cooling, heating, and power generation system driven by evacuated tube solar collectors for residential applications. Accordingly, evacuated tube collectors are employed to provide sufficient heat for cooling and power generation purposes, while the waste heat of the evacuated tube collectors is then exploited for heating. The proposed system is comprehensively analyzed from various standpoints i.e., energy, exergy, and exergoeconomic. Afterward, the multi-objective genetic algorithm optimization – as a suitable tool – is applied to the system to extract a trade-off between the competing objective functions. The results showed that R124 results in better exergetic efficiency compared to other refrigerants. The parametric study outcomes indicate that with increasing the collector area, total product cost reduces but the total cost rate increases dramatically. The results also show that input parameters can directly affect the exergetic sustainability index to be cautiously designed. The optimization results show that the system’s maximum exergy efficiency is 10.06% while the minimum total cost rate is obtained as 0.4835 $/h. Further, the decision factors’ scatter plots show that LiBr mass fraction should be around 25% for optimal operation. Overall, the proposed system can be employed for cooling, heating, and power generation as a potential cycle.

Roaming is when the mobile user goes out of his/her home agent network coverage and loses its signal. Loss of coverage and signals may be limited to a remote area or may occur when mobile user leaves the country and moves to a country where his/her mobile carrier network is not available. In this case, the mobile device is in roaming mode. In this mode, mobile user through connection to a Foreign Agent can still use its home agent services if his/her authentication be successful. In such situations, the authentication mechanism plays a key and important role, where the mobile user often needs to integrate and secure roaming service over multiple foreign agents. Designing a secure mechanism in Global Mobility Network (GLOMONET) is a difficult and complex task due to the computational and processing limitations of most mobile devices, as well as the wireless nature of communication environment. Unfortunately, most of the authentication schemes that have been proposed so far to meet this goal have failed to achieve their goal. In this line, Shashidhara et al. recently reported security vulnerabilities of Xu et al.’s mobile authentication scheme, and also presented an amended version of it. This paper shows that this proposed scheme has security flaws against impersonation, traceability, forward secrecy contradiction, and stolen smart card attacks, which implies that this protocol may not be a proper choice to be used on GLOMONET. On the other hand, we propose AMAPG, as a cost-efficient remedy version of the protocol which provides desired security against various attacks and also prove its security using BAN logic. We also evaluate AMAPG’s security using Scyther as a widely used formal tool to evaluate the security correctness of the cryptographic protocols.

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.