A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

A Performance Comparison of Classification Algorithms for Rose Plants.

One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets.

People's lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people's interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

Incorporation of engineered nanoparticles of biochar and fly ash against bacterial leaf spot of pepper.

In agriculture, the search for higher net profit is the main challenge in the economy of the producers and nano biochar attracts increasing interest in recent years due to its unique environmental behavior and increasing the productivity of plants by inducing resistance against phytopathogens. The effect of rice straw biochar and fly ash nanoparticles (RSBNPs and FNPs, respectively) in combination with compost soil on bacterial leaf spot of pepper caused by Xanthomonas campestris pv. vesicatoria was investigated both in vitro and in vivo. The application of nanoparticles as soil amendment significantly improved the chili pepper plant growth. However, RSBNPs were more effective in enhancing the above and belowground plant biomass production. Moreover, both RSBNPs and FNPs, significantly reduced (30.5 and 22.5%, respectively), while RSBNPs had shown in vitro growth inhibition of X. campestris pv. vesicatoria by more than 50%. The X-ray diffractometry of RSBNPs and FNPs highlighted the unique composition of nano forms which possibly contributed in enhancing the plant defence against invading X. campestris pv. vesicatoria. Based on our findings, it is suggested that biochar and fly ash nanoparticles can be used for reclaiming the problem soil and enhance crop productivity depending upon the nature of the soil and the pathosystem under investigation.

A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

Home automation using general purpose household electric appliances with Raspberry Pi and commercial smartphone.

This study presents the design and implementation of a home automation system that focuses on the use of ordinary electrical appliances for remote control using Raspberry Pi and relay circuits and does not use expensive IP-based devices. Common Lights, Heating, Ventilation, and Air Conditioning (HVAC), fans, and other electronic devices are among the appliances that can be used in this system. A smartphone app is designed that helps the user to design the smart home to his actual home via easy and interactive drag & drop option. The system provides control over the appliances via both the local network and remote access. Data logging over the Microsoft Azure cloud database ensures system recovery in case of gateway failure and data record for lateral use. Periodical notifications also help the user to optimize the usage of home appliances. Moreover, the user can set his preferences and the appliances are auto turned off and on to meet user-specific requirements. Raspberry Pi acting as the server maintains the database of each appliance. HTTP web interface and apache server are used for communication between the android app and raspberry pi. With a 5v relay circuit and micro-processor Raspberry Pi, the proposed system is low-cost, energy-efficient, easy to operate, and affordable for low-income houses.

Endoscopic Removal of an Uncommon Iatrogenic Foreign Body from the Maxillary Sinus: A Dental Burr.

Instrument fracture during procedure is not uncommon for dental surgeons, especially in root canal surgeries, usually inside the root canals. In rare instances, high-speed rotary instruments can be fractured and can be dislodged in key anatomical areas of face. In our case report, a high-speed dental burr most probably penetrated the root and was seen in the left maxillary sinus during a likely routine dental procedure. The work-up and endoscopic surgical management of the case is described. Practitioners should be in great care during dental procedures and endodontic treatment to avoid unexpected complications by introducing foreign bodies into maxillary sinus. Any patient presenting with recurrent unilateral facial pain or unilateral sinus symptoms with/without previous history of sinusitis should raise the suspect of a foreign body in the paranasal sinus regardless of any previous history of dental procedure.

The benefit of trans-attic endoscopic control of ossicular prosthesis after cholesteatoma surgery.

OBJECTIVE: To show the efficiency of using transmastoid atticotomy (TMA) endoscopy on the outcome of ossiculoplasty in patients with cholesteatoma. TMA is often performed as part of the surgical management of patients with middle ear cholesteatoma extending to the epitympanum. TMA can also be used as an access for endoscopic view to confirm the right alignment and stability of the ossicular prosthesis because the reconstruction of the tympanic membrane will obscure the visualization of the prosthesis. METHODS: A retrospective study was done at a tertiary referral institute, including 133 ears with cholesteatoma that underwent canal wall-up tympanomastoidectomy (CWU) with ossicular reconstruction using titanium prosthesis between August 2013 and August 2015. Post packing of the ear canal and position, stability, and axis of the prosthesis were checked using endoscope positioned in the attic through TMA. A postoperative pure-tone average air-bone gap (ABG) of 20 dB or less was considered as a successful hearing result. Results are compared with historical control groups. RESULTS: Of the 133 ears, 88 patients underwent reconstruction with partial ossicular replacement prosthesis (PORP), whereas the rest (45 patients) had total ossicular replacement prosthesis (TORP). A postoperative ABG ≤ 20 dB was obtained in 77.4% of all the patients (79.5% for PORP; 73.3% for TORP). CONCLUSION: Endoscopic assessment of the ossicular prosthesis via the attic, after repositioning of the tympanomeatal flap and packing the ear canal, decreases the risk of immediate ossiculoplasty failure and improves the functional outcome after ossicular chain reconstruction in cholesteatoma surgery. LEVEL OF EVIDENCE: 4 Laryngoscope, 129:2754-2759, 2019.

Chemical Closure of Tympanic Membrane Perforation: Call for Caution.

Chemical closure of tympanic membrane perforation is a commonly practiced office-based otological procedure, which is labeled to be effective and safe. In this paper, we report a case of a young lady with disastrous complications following an attempt of chemical cauterization of her perforated tympanic membrane.

Preoperative Predictors in Chronic Suppurative Otitis Media for Ossicular Chain Discontinuity: A Cross-Sectional Study.

OBJECTIVE: Ossicular discontinuity may result from chronic suppurative otitis media and is usually detected intraoperatively. Our objective is to determine whether a preoperative audiogram can preoperatively predict the presence or absence of ossicular discontinuity. METHODS: A cross-sectional study was prospectively run on our patients, aged 12-75 years, ultimately operated on for chronic suppurative otitis media. Preoperative audiograms were analyzed to measure frequency-specific air-bone gap (ABG) cutoff values. Intraoperatively, ossicular chain integrity was carefully checked. Logistic regression analysis was done to obtain a predictive model. RESULTS: A total of 270 patients (306 ears) were included. Frequency-specific ABG cutoff values can predict ossicular discontinuity, namely: high ABGs at 1,000 Hz (>27.5 dB) and 2,000 Hz (>17.5 dB) are the most reliable variables associated with ossicular discontinuity. CONCLUSION: Preoperative audiograms can predict the presence of ossicular discontinuity in chronic suppurative otitis media. Large ABGs at both 1,000 and 2,000 Hz can predict ossicular discontinuity with a great degree of certainty.