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Rife acceptance of Cloud Computing has made it bull’s eye for the hackers. Intrusion detection System (IDS) plays a vibrant role for it. Researchers have done marvelous works on the development of a competence IDS. But there are many challenges still exists with IDS. One of the biggest concerns is that the computational complexity and false alarms of the IDS escalates with the increase in the number of features or attributes of the dataset. Hence, the concept of Feature Selection (FS) contributes an all-important role for the buildout of an efficacious IDS. New FS algorithm is put forward which is the modified Firefly Algorithm in which Decision Tree (DT) classifier is used as the classification function. We have used the hybrid classifier which is the combination of neural network and DT. We have used CSE CIC IDS 2018 dataset and simulated dataset for performance assessment. Our examination pragmatic that the performance of proposed architecture is better than the state-of-the-art algorithms.

Non-invasive microwave technologies are emerging in biomedical applications. One of the promising applications is muscular activities detection, where the microwave spectrum of sub-5 GHz is primarily exploited due to large skin penetration depth. However, spatial resolution is limited, and high range resolution is required to analyze electromagnetic signals reflected from the layered structure of the skin and muscle. The resolution is proportional to bandwidth, and thus this study examines a spectrum from 6 GHz to 100 GHz. Although the dielectric properties of skin and muscle can be derived from state-of-the-art models and measurements, they are inadequate for estimating the penetration losses of inhomogeneous multi-layered tissues. Thus, this study emphasizes the precise estimation of penetration losses through skin tissue layers and reflectivity from the muscle tissue surface, using porcine skin as a substitute for human tissue, with a novel focus on the volume scattering behavior. Skin tissue layers are analyzed in two forms, with the first including the epidermis and dermis layers, and the second additionally including the hypodermis or subcutaneous fat layer. In a validating approach, this study aims to compare measurement results obtained from the free-space method using a vector network analyzer with known tissue model parameters derived from previous works. Further, a novel aspect of this study is the derivation of a link budget estimation for muscle sensing based on estimated losses from skin and the radar cross section (RCS) of muscle. This provides a replicable framework and foundational principles for skin and muscle sensing in biomedical engineering.

This paper presents a modified bonobo optimizer (MBO) that integrates the Gaussian local mutation, restart strategy, and random contraction strategy into the original bonobo optimizer (BO). BO, inspired by the unique reproductive schemes and fission-fusion social behaviors of bonobos, has previously demonstrated promising results in solving a range of optimization problems. With the new modifications, MBO seeks to improve exploration and exploitation abilities, achieving enhanced convergence speed and solution quality. The Gaussian local mutation aids in fine-tuning solutions by introducing localized variations, the restart strategy provides a mechanism to escape potential local optima, while the random contraction strategy ensures better global search capabilities. The enhanced MBO’s performance is critically assessed on the 10 and 100-dimensional CEC 2017 and 10 and 20-dimensional CEC 2022 benchmark suites, along with seven engineering optimization problems, including cantilever beam design, industrial refrigeration system design, welded beam design, speed reducer design, pressure vessel design, multi-product batch plant design, and three-bar truss design. The MBO algorithm exhibits significant improvements in optimization performance, evidenced by highly significant p-values (as low as 1.25E-11) in the Wilcoxon’s Signed Rank Test. Preliminary results indicate that the MBO exhibits a marked improvement in both solution accuracy and robustness over its predecessor and other state-of-the-art optimization algorithms such as original bonobo optimizer, sand cat swarm optimization, Chernobyl disaster optimizer, driving training-based optimization, Harris hawk optimizer, Archimedes optimization algorithm, smell agent optimizer, grasshopper optimization algorithm, particle swarm optimization, hybrid sine cosine algorithm with differential evolution, modified capuchin search algorithm, liver cancer algorithm, and modified chameleon swarm algorithm. The algorithm’s robust performance can be attributed to its accelerated convergence rate, stability across diverse functions, good exploration-exploitation behavior, and adaptability to high-dimensional and complex solution spaces. The systematic enhancement of proposed algorithm’s convergence capabilities positions it as a reliable and efficient tool for addressing challenging engineering optimization problems.

In this paper, new concepts for continuous 24/7 real-time monitoring of plants and insects with miniaturized terahertz (THz) systems are described and experimentally tested. Thus, for the first time, small-scale insights into the instantaneous plant health but also in their long-term growth can be obtained. Using such compact THz systems, e.g. water uptake, insect infestation and the behavior of pollinators (i.e. honey bees) and pests can be measured dynamically and non-invasively at virtually any position in the close biotope surrounding them. In addition to general understanding, this can be used to optimize crop yield and reduce resource consumption as well as for identifying characteristics of insect-plant interactions induced by potential environmental stressors. Given such a holistic approach, the proposed concepts may provide a significant advancement in environmental monitoring technology.

One of those illnesses that is most deadly to people is cancer. The only way to prevent any harm to humanity is by its early discovery and treatment. Various types of tests are conducted in the medical labs for the detection of cancer. Cancer can also be detected at the genetic level. For this distinct machine learning and deep learning methods already exist. This paper proposes a hybrid method based on Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) to predict different types of cancer such as Breast, Lung, Uterine, Kidney, Prostate and colon cancer from gene expression data. The bottleneck features are extracted using the sandwich stacked method based on VGG16 and VGG19 pre-trained models. Afterward, the proposed hybrid classifier based on RNN-CNN has been used to classify the data into various classes. The proposed model performs better than the other existing methods such as VGG16, VGG19, ResNet50, Inception V3 and MobileNet classifier in terms of various performance metrics such as accuracy, Mean Square Error (MSE), precision, recall, and F1 score. RNN-CNN classifier provides the highest accuracy of 0.978 among all the other existing methods for Dataset 1 and the highest accuracy of 0.994 for Dataset 2 at 80% training data. On the other hand, RNN-CNN classifier provides the lowest MSE of 0.101 among all the other existing methods for Dataset 1 and the lowest MSE of 0.006 for Dataset 2 at 80% training data.

Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.

In radar remote sensing, the Terahertz (THz) spectrum is presently being investigated worldwide with focus on short-range indoor and outdoor applications. The spectrum broadens the unmanned aerial vehicle (UAV) based synthetic aperture radar (SAR) applications to indoor room profiling with sub-mm resolution and material characterization as many materials have unique fingerprints at this spectrum. SAR technique requires precise localization information of the mobile radar sensor, which in conventional SAR is achieved using an existing localization infrastructure, such as a global positioning system (GPS) and inertial measurement unit (IMU). For the indoor THz SAR, the GPS does not provide coverage in indoor complex environments, and also the state-of-art compact IMU does not provide the required sub-mm accuracy. These limitations can be overcome by utilizing an indoor localization system. Therefore, this paper presents an indoor THz simultaneous localization and mapping (SLAM) system. The system comprises of passive tags based radio frequency identification (RFID) localization system and SAR that provides the UAV localization and mapping of the in-room objects. Another challenge for the UAV based indoor THz SAR that is addressed in this paper is motion compensation (MOCO). At the THz, MOCO requires special consideration due to very small trajectory deviation is in the range of carrier wavelength. Therefore, to study the effects of the sub-mm translational errors, a testbed has been set up, and measurement results are presented in this paper along with the 3D electromagnetic simulation results for a carrier frequency of 275 GHz and bandwidth of 50 GHz. Further, to compensate these errors, the sub-mm localization system is used and the results are presented to validate the proposed solution for indoor THz SAR MOCO.