A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security.

Ensuring the privacy and trustworthiness of smart city-Internet of Things (IoT) networks have recently remained the central problem. Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. However, it limits the critical problems of high time complexity, computational cost, difficulty to understand, and reduced level of security. Therefore, the proposed work intends to implement a group of novel methodologies for developing an effective Cyborg intelligence security model to secure smart city systems. Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. The original contribution of this work is to develop a novel Cyborg intelligence framework for protecting smart city networks from modern cyber-threats. In this system, a combination of unique and intelligent mechanisms are implemented to ensure the security of smart city networks. It includes QIDI for data filtering, CSOM for feature optimization and dimensionality reduction, and RMML for categorizing the type of intrusion. By using these methodologies, the overall attack detection performance and efficiency have been greatly increased in the proposed cyborg model. Here, the main reason of using CSOM methodology is to increase the learning speed and prediction performance of the classifier while detecting intrusions from the smart city networks. Moreover, the CSOM provides the optimized set of features for improving the training and testing operations of classifier with high accuracy and efficiency. Among other methodologies, the CSOM has the unique characteristics of increased searching efficiency, high convergence, and fast processing speed. During the evaluation, the different types of cyber-threat datasets are considered for testing and validation, and the results are compared with the recent state-of-the-art model approaches.

Attention-based bidirectional-long short-term memory for abnormal human activity detection.

Abnormal human behavior must be monitored and controlled in today's technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.

Pneumonia detection with QCSA network on chest X-ray.

Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.

Disaster management ontology- an ontological approach to disaster management automation.

The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA's responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users.

Towards improving e-commerce customer review analysis for sentiment detection.

According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as 'Positive', 'Neutral', 'Negative', or 'Indifferent'. It analyses the data and labels the 'better' and 'worse' assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.

Deep Learning Approaches for Prognosis of Automated Skin Disease.

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.