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The categorization and identification of lung disorders in medical imageries are made easier by recent advances in deep learning (DL). As a result, various studies using DL to identify lung illnesses were developed. This study aims to analyze different publications that have been contributed to in order to recognize lung cancer. This literature review examines the many methods for detecting lung cancer. It analyzes several segmentation models that have been used and reviews different research papers. It examines several feature extraction methods, such as those using texture-based and other features. The investigation then concentrates on several cancer detection strategies, including “DL models” and machine learning (ML) models. It is possible to examine and analyze the performance metrics. Finally, research gaps are presented to encourage additional investigation of lung detection models.

The use of Application-Specific Instruction-set Processors (ASIP) in embedded systems is a solution to the problem of increasing complexity in the functions these systems have to implement. Architectures based on Very Long Instruction Word (VLIW) have found fertile ground in multimedia electronic appliances thanks to their ability to exploit high degrees of Instruction Level Parallelism (ILP) with a reasonable trade-off in complexity and silicon costs. In this case the ASIP specialization involves a complex interaction between hardware- and software-related issues. In this paper we propose tools and methodologies to cope efficiently with this complexity from a multi-objective perspective. We present EPIC-Explorer, an open platform for estimation and system-level exploration of an EPIC/VLIW architecture. We first analyze the possible design objectives, showing that it is necessary, given the fundamental role played by the VLIW compiler in instruction scheduling, to evaluate the appropriateness of ILP-oriented compilation on a case-by-case basis. Then, in the architecture exploration phase, we will use a multi-objective genetic approach to obtain a set of Pareto-optimal configurations. Finally, by clustering the configurations thus obtained, we extract those representing possible trade-offs between the objectives, which are used as a starting point for evaluation via more accurate estimation models at a subsequent stage in the design flow.

Embedded distributed multimedia applications based on the use of on-chip networks for communication and messages exchange requires specific and enhanced quality of service (QoS) management. To reach the desired performances at the application level, the network-on-chip (NoC) router should implement per flit handling strategy with wide granularity. This purpose requires an enhanced internal architecture that ensures from one hand a specific management according to a service classification and from the other hand, it enhances the routing process.

In this context, this paper proposes a new mechanism for QoS management in NoC. This mechanism is based on the use of central memory where flits are in-queued according to their class of service. This scheme enables an optimal flit scheduling phase and provides more capabilities to drop low important flits when the router shows congestion state symptoms. The paper presents, also, a protocol structure that fills with this architecture and introduces a signaling mechanism to make efficient the QoS management through the proposed architecture. The circuit performances and its adaptability to achieve QoS with low power processing and high bandwidth in on chip multiprocessor systems will be studied in this paper.

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning.

The goal of SCM is to apply decisions, which end up with optimal organizational performance. Supply chain (SC) exists in service, manufacturing, and business organizations. Managing the reliability of processes and products in multi-stakeholder SC surroundings is a major challenge nowadays. As it provides protected control and traceability, trust, and immutability establishment among stakeholders with less-expensive solutions, blockchain has recently emerged as a most essential mechanism. This study critically evaluates 65 papers that discuss BCT in SCM, more specifically, an analysis of several performance measurements that are included in various papers is carried out. Additionally, a thorough analysis of the highest performance levels and various technical elements used in each task is conducted. Finally, the survey is extended with the identification of various research challenges, which may help the analysts to support improved future works on BCT in SCM. Chronological analysis is also performed.