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Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques. [ABSTRACT FROM AUTHOR]

A salinity gradient solar pond (SGSP) is capable of storing a significant quantity of heat for an extended period of time. It is a great option for providing hot water at a reduced energy cost. Additionally, SGSP is used in low-temperature industrial applications such as saltwater desalination, space heating, and power generation. Solar pond thermal performance is dependent on a variety of operational variables, including the soil conditions, the climate of the particular site, the thickness of the solar pond layers, the depth of the water table, and the salt content of the pond. As such, this study examines the thermal performance of a solar pond under a variety of operational conditions. The solar pond model is used to test the thermal performance by simulating two-dimensional heat and mass transport equations. The equations are solved using the finite difference technique utilizing MATLAB® scripts. Salt distributions and temperature profiles are computed for a variety of factors influencing SGSP's thermal performance. The main distinguishing variables influencing the thermal performance of SGSP are soil conditions, such as soil texture, types, the moisture level in soil, and water table depth. The final findings indicated that the fine sand dry soil performed better than the other soil types owing to its poor heat conductivity. The economic results indicated that the period of return (POR) of the intended system is around 2 years. The solar pond construction costs such as excavation, transportation, salt and lining, were considered based on the local prices. This modeled study extracted the greatest possible energy is 110W/m2, with the fine sand dry at 62.48°C lowest temperature. This study suggested that the climatic conditions of Lahore is better than climatic conditions of Islamabad. Additionally, deeper water tables are suggested for improved thermal performance of the pond. [ABSTRACT FROM AUTHOR]

The thriving adoption of drones for delivering parcels, packages, medicines, etc., is surging with time. The application of drones for delivery services results in faster delivery, fuel-saving, and less energy consumption. Giant companies like Google, Amazon, Facebook, etc., are actively working on developing, testing, and improving drone-based delivery systems. So far, a lot of work has been done for improving the design, speed, operating range, security of the delivery drones, etc. However, very limited work has been done to ensure a complete and reliable last-mile delivery from the merchant's store to the hands of the actual customer. To ensure a complete and reliable last-mile delivery, a drone must authenticate the consumer before dropping the package. Therefore, in this work, we propose a consumer authentication (Consumer-Auth) hybrid computing framework for drone delivery as a service to make sure that the parcel is perfectly delivered to the intended customer. The proposed Consumer-Auth framework enables a drone to reach the exact destination by using the GPS coordinates of the customer autonomously. After reaching the exact location, the drone waits for the customer to come to the specific pinned location then it starts a two-factor consumer authentication process, i.e., one-time password (OTP) verification and face Recognition. The experimental results manifest the effectiveness of the proposed Consumer-Auth framework to ensure a complete and reliable drone-based last-mile delivery. [ABSTRACT FROM AUTHOR]

Cryptography is commonly used to secure communication and data transmission over insecure networks through the use of cryptosystems. A cryptosystem is a set of cryptographic algorithms offering security facilities for maintaining more cover-ups. A substitution-box (S-box) is the lone component in a cryptosystem that gives rise to a nonlinear mapping between inputs and outputs, thus providing confusion in data. An S-box that possesses high nonlinearity and low linear and differential probability is considered cryptographically secure. In this study, a new technique is presented to construct cryptographically strong 8×8 S-boxes by applying an adjacency matrix on the Galois field GF(28). The adjacency matrix is obtained corresponding to the coset diagram for the action of modular group PSL(2,Z) on a projective line PL(F7) over a finite field F7. The strength of the proposed S-boxes is examined by common S-box tests, which validate their cryptographic strength. Moreover, we use the majority logic criterion to establish an image encryption application for the proposed S-boxes. The encryption results reveal the robustness and effectiveness of the proposed S-box design in image encryption applications. [ABSTRACT FROM AUTHOR]