Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

3D Semantic Deep Learning Networks for Leukemia Detection

White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness.     

Instagram Mobile Application Digital Forensics

In this research, we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application, Instagram. This plugin extracts personal details from Instagram users, e.g., name, user name, mobile number, ID, direct text or audio, video, and picture messages exchanged between different Instagram users. While developing the plugin, we identified resources available in both Android and IOS-based devices holding key forensics artifacts. We highlighted the poor privacy scheme employed by Instagram. This work, has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed, and how the traces, as well as the URL links of visual messages, can be used to access the privacy of any Instagram user without any critical credential verification. We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file, which any criminal mind can use to set up or trap someone else. The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.     

Mysterious outbreaks of gastrointestinal illness associated with burritos supplied through school lunch programs

From October 1997 through March 1998, three outbreaks of gastrointestinal illness among school children were linked to company A burritos. In September 1998, a similar outbreak occurred in three North Dakota schools following lunches that included company B burritos. We conducted an investigation to determine the source of the North Dakota outbreak, identify other similar outbreaks, characterize the illness, and gather evidence about the cause. The investigation included epidemiologic analyses, environmental investigation, and laboratory analyses. In North Dakota, a case was defined as nausea, headache, abdominal cramps, vomiting, or diarrhea after lunch on 16 September 1998. Case definitions varied in the other states. In North Dakota, 504 students and staff met the case definition; predominant symptoms were nausea (72%), headache (68%), abdominal cramps (54%), vomiting (24%), and diarrhea (16%). The median incubation period was 35 min and median duration of illness was 6 h. Eating burritos was significantly associated with illness (odds ratio, 2.6; 95% confidence interval, 1.6 to 4.2). We identified 16 outbreaks that occurred in seven states from October 1997 through October 1998, affecting more than 1,900 people who ate burritos from two unrelated companies. All tortillas were made with wheat flour, but the fillings differed, suggesting that tortillas contained the etiologic agent. Results of plant inspections, tracebacks, and laboratory investigations were unrevealing. More than two million pounds of burritos were recalled or held from distribution. The short incubation period, symptoms, and laboratory data suggest that these outbreaks were caused by an undetected toxin or an agent not previously associated with this clinical syndrome. Mass psychogenic illness is an unlikely explanation because of the large number of sites where outbreaks occurred over a short period, the similarity of symptoms, the common food item, the lack of publicity, and the link to only two companies. A network of laboratories that can rapidly identify known and screen for unknown agents in food is a critical part of protecting the food supply against natural and intentional contamination.     

Arithmetic Optimization with Ensemble Deep Transfer Learning BasedMelanoma Classification

Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.     

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such as You Only Look Once (YOLO)v2 and Open Exchange Neural (ONNX) model. The localization model is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain.     

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things (IoT) has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices. The e-healthcare application solely depends on the IoT and cloud computing environment, has provided several characteristics and applications. Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing, which led to quick exhaustion of energy. In this view, this paper introduces a new energy efficient cluster enabled clinical decision support system (EEC-CDSS) for embedded IoT environment. The presented EEC-CDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process. The EEC-CDSS model incorporates particle swarm optimization with levy distribution (PSO-L) based clustering technique, which clusters the set of IoT devices and reduces the amount of data transmission. In addition, the IoT devices forward the data to the cloud where the actual classification procedure is performed. For classification process, variational autoencoder (VAE) is used to determine the existence of disease or not. In order to investigate the proficient results analysis of the EEC-CDSS model, a wide range of simulations was carried out on heart disease and diabetes dataset. The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.