Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed Parallel Machine scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and Machine availability after repair and maintenance. Failure times have been predicated on using Machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five Machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of Parallel Machine scheduling operations. After the previous data were received in the first step, Machine failure was predicted by using Machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these failures and Machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction.

Considering maintenance strategy in models which schedule and allocate jobs to Machines, will make the proposed models compatible with production environments. Furthermore, this will cause higher model efficiency in optimizing the production systems. To this end, a mathematical model for scheduling unrelated Parallel Machines is developed to minimize total weighted completion times. Also in this approach, availability constraints have been considered, and preemption is allowed. Due to executing preventive maintenance and emergency maintenance programs, Machine inaccessible times have been added to job completion times. Since the proposed model has high complexity, in order to solve the problem, two meta-heuristic methods including simulated annealing and genetic algorithm are used. In addition, their performances are compared to each other. The results indicate the superiority of simulated annealing over genetic algorithm for this particular problem.

Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional Parallel Machine-learning model for the dynamic detection of Android malware categories and families. Standard Machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many Machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.

A Machine translation system using hybrid approach was presented in this paper. This hybrid system consists statistical and rule- based approach in serial. English words order was changed by rule- based parts of system, and then trained by statistical approach. Cross in alignment of statistical approach was reduced by change words order of source side, so on translation quality was improved. Synchronized tree adjoining grammars was used for change words order in source side. These grammars were extracted automatically from aligned Parallel corpus. The system was tested by three types of test set and the results shows translation quality was improved by proposed Machine translation.

The relentless growth of global energy consumption poses a multitude of complex challenges, including the depletion of finite energy resources and the exacerbation of greenhouse gas emissions, which contribute to climate change. In the face of these pressing environmental concerns, the manufacturing sector, a significant energy consumer, is under immense pressure to adopt sustainable practices. The critical intersection of energy consumption management and production operation scheduling emerges as a pivotal domain for addressing these challenges. The scheduling of common operations, exemplified by the cutting stock problem in industries like furniture and apparel, represents a prevalent challenge in production environments. For the first time, this paper pioneers an investigation into an identical Parallel Machine scheduling problem, taking into account common operations to minimize total energy consumption and total completion time concurrently. For this purpose, two bi-objective mixed integer linear programming models are presented, and an augmented ε – constraint method is used to obtain the Pareto optimal front for small-scale instances. Considering the NP-hardness of this problem, a non-dominated sorting genetic algorithm (NSGA-II) and a hybrid non-dominated sorting genetic algorithm with particle swarm optimization (HNSGAII-PSO) are developed to solve medium- and large-scale instances to achieve good approximate Pareto fronts. The performance of the proposed algorithms is assessed by conducting computational experiments on test problems. The results demonstrate that the proposed HNSGAII-PSO performs better than the suggested NSGA-II in solving the test problems.

This paper considers the problem of scheduling Parallel Machines for split jobs to minimize the total tardiness. Accepting a new job, each Machine needs to be set up and the setup times depend on the sequence of jobs. To solve the above problem, a new approach is suggested and a number of theorems are provided and proved regarding resource planning and job sequencing for the given problem in hand. Then, the proposed algorithm is verified and evaluated with a number of test problems. The associated results are analyzed and compared with the results obtained by the Lingo software.

Parallel mechanisms are more utilized, because of their extra precision and stiffness in comparison with serial mechanisms. Measuring position and orientation (pose) of Parallel robot end effector and performing kinematic calibration, is a guaranty for, moving precision of these robots. Through different measurement devices, Machine vision has advantages such as low cost, simplicity in use and rather appropriate precision. In this research, in order to measure pose of end effector of a 4-DOF (degree of freedom) Parallel robot, a vision-based measurement system, is designed and implemented in Matlab software. The laboratory device is a stereo camera. Camera calibration is carried out in Matlab. A triangular shape is used as feature on the robot end effector. By processing the images acquired from pose of end effector, in designed measurement system, rotation matrix and translation vector are obtained and the exact position of the end effector is revealed. For investigating precision and accuracy of proposed measurement system, a conventional measurement method with the help of gauge block and indicator, is used. According to the nature of Machine vision, the results achieved in this examination, are acceptable; although better precision can be attained by improving devices used in this research.

In the research, six degrees of freedom Hexapod Parallel Machine tool is studied and investigated. Jacobian matrix is developed by cinematic relations differentiation and using weighted coefficient method, dimensional analysis operation is carried out on Jacobian matrix. Geometric parameters of Cartesian robot workspace are optimized, considering a minimum allowable rotation about three axes, and implementing Genetic Algorithm in MATLAB software workspace. For the manipulator workspace, isotropy indices, minimum and maximum singular values are calculated. The optimization operation has lead to two different designs of manipulators, namely the isotropic design, and high resolution Cartesian workspace.

Since the production scheduling is covered the wide range of manufacturing and services systems, the types of related issues are highly diverse. In advanced manufacturing environments, because the production flexibility property is taking into account as a competitive advantage, in this paper a special kind of flexibility in the job shop scheduling problem is defined in which, for jobs processing, each workstation have multiple Parallel Machines. Processing speed of each Machine can also be different. The objective of this problem is to minimize the maximum completion time (makespan). Due to NPhardness of problem, we proposed a metaheuristic algorithm. In the proposed approach, due to the structure of the problem and its discrete enviorment, we modified a particle swarm optimization as a new discrete algorithm. Finally to evaluate the performance of the algorithm, the proposed algorithm has been compared with several heuristics existing in the literature.