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Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods. [ABSTRACT FROM AUTHOR]

This paper discusses the Internet of Things (IoT) and the security challenges associated with it. IoT is a network of interconnected devices that share information. However, the low power and resources of IoT devices make them vulnerable to attacks. Using heavy protocols like HTTP for IoT devices can prove costly and using popular lightweight protocols like CoAP can invite attacks such as DoS (Denial-of-Service). While security models such as DTLS and LSPWSN can secure IoT against such attacks, they also have limitations. To overcome this problem, this paper proposes a machine learning model that detects DoS amplification attacks against CoAP with 99% accuracy. To the best of our knowledge, this research is the first to use the multi-classification process to detect and classify the different types of the DoS amplification techniques that attack CoAP client use against victim CoAP clients. [ABSTRACT FROM AUTHOR]

Organizations are currently more dependent on Information and Communication Technology (ICT) resources. The main purpose of this research is to help the organization in order to maintain the quality of their ICT project based on evaluation criteria presented in this research. This paper followed several steps to support the methodology section. Firstly, an experimental investigation conducted to explore the values assessment criterion, an organization may realize from ICT project such as information systems, enterprise systems and IT infrastructure. Secondly, the investigation is further based on empirical data collected and analyzed from the respondents of six case studies using questionnaire based on the findings of literature review. Finally this paper propose the development of a holistic model for assessing business values of ICT from the managerial point of view based on measured factors. The study has contributed in this field practically and theoretically, as the literature has not shown a holistic approach of used eight distinct dimensions for assessing ICT impact over business values. It has combined the previous researches in a manner to extend the dimensions of measuring ICT business values. The model has shown its significance for managers and ICT decision makers to align between business strategies and ICT strategies. The findings suggest that ICT positively support business processes and several other business values dimensions. The proposed holistic model and identified factors can be useful for managers to measure the impact of emerging ICT on business and organizational values. [ABSTRACT FROM AUTHOR]