Certificateless generalized signcryption adaptively work as certificateless signcryption, signature or encryption scheme having single algorithm for suitable storage-constrained environments. Recently, Zhou et al. proposed a novel Certificates generalized scheme, and proved its ciphertext indistinguishability under adaptive chosen ciphertext attacks (IND-CCA2) using Gap Bi-linear Diffie-Hellman and Computational Diffie-Hellman assumption as well as proved existential unforgeability against chosen message attacks (EUF-CMA) using the Gap Bi-linear Diffie-Hellman and Computational Diffie-Hellman assumption in random oracle model. In this paper, we analyzed Zhou et al. scheme and unfortunately proved IND-CCA2 insecure in encryption and signcryption modes in defined security model. We also present a practical and improved scheme, provable secure in random oracle model.

Spatial and temporal information on urban infrastructure is essential and requires various land-cover/land-use planning and management applications. Besides, a change in infrastructure has a direct impact on other land-cover and climatic conditions. This study assessed changes in the rate and spatial distribution of Peshawar district's infrastructure and its effects on Land Surface Temperature (LST) during the years 1996 and 2019. For this purpose, firstly, satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper) plus and OLI (Operational Land Imager) of 30 m resolution were taken. Secondly, for classification and image processing, remote sensing (RS) applications ENVI (Environment for Visualising Images) and GIS (Geographic Information System) were used. Thirdly, for better visualization and more in-depth analysis of land sat images, pre-processing techniques were employed. For Land use and Land cover (LU/LC) four types of land cover areas were identified -vegetation area, water cover, urbanized area, and infertile land for the years under research. The composition of red, green, and near infra-red bands was used for supervised classification. Classified images were extracted for analyzing the relative infrastructure change. A comparative analysis for the classification of images is performed for SVM (Support Vector Machine) and ANN (Artificial Neural Network). Based on analyzing these images, the result shows the rise in the average temperature from 30.04 • C to 45.25 • C. This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km 2 from 1996 to 2019. It has also been witnessed that

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.

Coronary Artery Disease is the type of cardiovascular disease (CVD) that happens when the blood vessels which stream the blood toward the heart, either become tapered or blocked. Of this, the heart is incapable to push sufficient blood to encounter its requirements. This would lead to angina (chest pain). CVDs are the leading cause of mortality worldwide. According to WHO, in the year 2019 17.9 million people deceased from CVD. Machine Learning is a type of artificial intelligence that uses algorithms to help analyse large datasets more efficiently. It can be used in medical research to help process large amounts of data quickly, such as patient records or medical images. By using Machine Learning techniques and methods, scientists can automate the analysis of complex and large datasets to gain deeper insights into the data. Machine Learning is a type of technology that helps with gathering data and understanding patterns. Recently, researchers in the healthcare industry have been using Machine Learning techniques to assist with diagnosing heart-related diseases. This means that the professionals involved in the diagnosis process can use Machine Learning to help them figure out what is wrong with a patient and provide appropriate treatment. This paper evaluates different machine learning models performances. The Supervised Learning algorithms are used commonly in Machine Learning which means that the training is done using labelled data, belonging to a particular classification. Such classification methods like Random Forest, Decision Tree, K-Nearest Neighbour, XGBoost algorithm, Naive Bayes, and Support Vector Machine will be used to assess the cardiovascular disease by Machine Learning.