Social media is considered as a platform whereusers express themselves. The rise of social me-dia as one of humanity’s most important publiccommunication platforms presents a potentialprospect for early identification and manage-ment of mental illness. Depression is one suchillness that can lead to a variety of emotionaland physical problems. It is necessary to mea-sure the level of depression from the socialmedia text to treat them and to avoid the nega-tive consequences. Detecting levels of depres-sion is a challenging task since it involves themindset of the people which can change period-ically. The aim of the DepSign-LT-EDI@ACL-2022 shared task is to classify the social me-dia text into three levels of depression namely“Not Depressed”, “Moderately Depressed”, and“Severely Depressed”. This overview presentsa description on the task, the data set, method-ologies used and an analysis on the results ofthe submissions. The models that were submit-ted as a part of the shared task had used a va-riety of technologies from traditional machinelearning algorithms to deep learning models.It could be observed from the result that thetransformer based models have outperformedthe other models. Among the 31 teams whohad submitted their results for the shared task,the best macro F1-score of 0.583 was obtainedusing transformer based model.

Findings of the Shared Task on Detecting Signs of Depression from Social Media

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.

Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm

The ever-increasing interconnectedness of our world, fueled by technological advancements across industries, has made network security a paramount concern. This concern stems from the evolving tactics of cybercriminals and our dependence on interdependent systems. Extreme Learning Machines (ELMs) have recently been renamed Single-Hidden-Layer Feedforward Neural Networks (SLNFs) to achieve a rapid learning rate by randomly initializing weights and deviations. For a decade, researchers have been primarily focused on the investigations of ELM, which offers a distinctive use that prompted academics to investigate its possible application in various disciplines. Considering the Intrusion Detection System (IDS) framework, ELM provides an enticing path for constructing effective and adaptable IDS models capable of analyzing enormous amounts of network data in real-time. ELMs are based on feedforward networks using single or Multi-Hidden Layers (MHLs). ELM-based IDS assists in safeguarding networks, systems, and web applications against cyber-attacks, phishing, and cyber threats. As a result, during the last decade, ELM has taken center stage for development as an exciting technology for effective and accurate classification tasks. ELM represents a subset of the Machine Learning (ML) algorithms utilized by IDSs, which makes our work contribute significantly to the field of network security. First, we investigate the ELM-based IDS throughout the previous decade, including the idea, scope, and rationale of ELM. Second, our study aims to identify abnormal and malicious actions in network traffic while offering real-time protection against potential threats, including detecting Distributed Denial of Services (DDoS) attacks and phishing attacks that use malicious URLs and payloads for successful attacks. Finally, our work encompasses applications, extensions, attacks, security issues, and a comparative analysis showcasing the versatility of ELM within various algorithms. This underscores ELM's evolution as a promising technology for accurate and effective classification tasks, particularly in network security research. 

Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey

Not so many consumer devices are available for minimally invasive monitoring of Skin Conductance (SC), differently from what happens for other physiological signals. In this paper, a low-cost monitoring system for SC signals is presented. For comparison purposes, the SC signals are simultaneously acquired by the low-cost monitoring system and by a ProComp Infiniti desk equipment. The paper shows that, despite the simpler design and hardware limitations exhibited by the low-cost system, the collected SC signals provide the same relevant information for stimulus detection of the SC signals acquired by a much more expensive acquisition board. Specifically, the comparison is fulfilled through the ability of the low-cost monitoring system in detecting the increase of both SC baseline and peaks after stimulation. 

Low-cost monitoring for stimulus detection in skin conductance

Abstract Aiming at the problem of low accuracy and unstable prediction results caused by the random connection weight threshold between the input layer and the hidden layer of extreme learning machine (ELM). An adaptive dual-strategy improved pelican optimization algorithm (IPOA) -ELM regression prediction model is proposed. Firstly, the pelican optimization algorithm (POA) is improved by the Logistic-Tent chaotic map, improved convergence factor by adaptive double strategy method and reverse learning strategy. Then, the performance of IPOA is verified by multiple groups of multi-dimensional single-peak and multi-peak test functions. The test results show that IPOA has better accuracy, stability, and robustness than POA, butterfly optimization algorithm (BOA), cuckoo algorithm (CS), grey wolf algorithm (GWO), particle swarm optimization algorithm (PSO), genetic algorithm (GA) and mouse swarm optimization algorithm (RSO). Finally, IPOA is applied to optimize ELM, and the performance of the IPOA-ELM model is verified by three engineering data sets. The simulation results show that the convergence accuracy, stability, and robustness of the IPOA-ELM model are better than those of the POA-ELM and ELM models. 

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

The Internet of Things (IoT) is a global information and communication technology which aims to connect any type of device to the internet at any time and in any location. Nowadays billions of IoT devices are connected to the world, this leads to easily cause vulnerability to IoT devices. The increasing of users in different IoT-related applications leads to more data attacks is happening in the IoT networks after the fog layer. To detect and reduce the attacks the deep learning model is used. In this article, a hybrid sample selected recurrent neural network-extreme learning machine (hybrid SSRNN-ELM) algorithm that uses recurrent neural network (RNN) as a supervised and extreme learning machine (ELM) classifier as unsupervised. In the proposed algorithm sample selected features are extracting from the original dataset using linear regression with recursive feature extraction (LR-RFE) and sequence forward selector (SFS) then RNN is used to learn the behavior of the important features and at end layer the ELM classifier is used. This hybrid intrusion detection algorithm is placed in between the fog layer and its devices. NSL_KDD benchmark is used for detecting the distributed denial-of-service (DDoS) attack in IoT devices after the fog node. The proposed hybrid SSRNN-ELM model exposes the attacks while testing with enhanced accuracy of up to 99% from NSL-KDD data set. Experimental results outperform by using proposed technique when compared with the existing models. 

Detection of DDoS Attack in IoT Networks Using Sample Selected RNN-ELM

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.

SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models

In recent years, many research solutions offered to recognize human emotional states automatically. As per the report of the world healthcare organization, people fell unfortunately on the road and could not provide first aid on time, resulting in death. The proposed research design overcomes the emergency of emotion management system with the help of physiological sensors. The physiological sensors, such as Galvanic Skin Response (GSR) and Blood Volume Pressure (BVP) are used to identify emotion signals. Initially, the ESP32 controller captures the signal from GSR and BVP. Later, it will be converted into a digital data format. These collected sensor data are analyzed using the proposed selection algorithm and web dashboards for real-time emotion detection. The panic alarm is altered in a real-time scenario if the emotion exceeds a particular threshold. It sends the message to the concerned user with the live location using a Global Positioning System (GPS). Experimental results outperform with improved accuracy and less dependability by using physiological signals to measure indirect emotions in complex situations.

Design and Implementation of Selection Algorithm based Human Emotion Recognition System

The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNET. The XLNet model outperformed the other two models in terms of various performance metrics. The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.

SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models

Waste management is one of the important factors which determine the health of an urban/rural area. Maintaining the surroundings clean and tidy in waste management becomes a tedious task for the government. To resolve this issue intelligent monitoring takes immediate effect on solid waste dust bins for making a green and safe environment. In this paper, a design of an intelligent smart-based waste management system (ISWM) with a predicted forecast is proposed. The proposed smart bin model uses ultrasonic, flame, gas, and accelerometer sensors these data is processed by the low power microcontroller called ESP-32. Initially, all the sensor data are processed by using ESP32 and statistical data is stored in the cloud. The proposed system consists of two parts the first step is a notification of the smart bin status to the user by the Global Internet of Things (GIoT) and secondly, smart bin sensor data is captured to form a dataset and the level of bin status in the dataset monitored and interpreted by using multivariate Seasonal AutoRegressive Integrated Moving Average with eXogenous (SARIMAX) model. The proposed SARIMAX model forecasts the abnormality of the sensor data immediately sends the notification to the concerned user. In this work, the bin management framework is designed to monitor solid waste at all times. This work promotes cleanliness and efficient management of resources. Our proposed model performance metrics such as RMSE and MAPE are 0.6145 and 3.20 respectively, which shows the forecasting values have fewer deviations when compared to the actual values.

Design and Development of GIOT based Intelligent Smart Waste Management and Predictive Modelling

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