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Instructors’ perceptions of the development of work-readiness through simulations

- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew

  • Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
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  • Description: The global ERP software market is expected to reach $117.09 billion by 2030 (Biel, July 12, 2022). To boost graduate work-readiness, Australian institutions are adopting new pedagogical strategies by familiarising Information systems (IS) students with this highly sought-after software. One of these techniques is simulation games that provide students with a risk-free, real-world simulation of popular software to develop soft and hard skills needed by the IS industry. This exploratory study employed the Grounded Theory approach to evaluate instructors' perceptions of the influence of simulation games on the work-readiness of information systems students. We conducted semi-structured interviews with (Enterprise Resource Planning Simulation) ERPsim game laboratory instructors. The authors utilised Work Readiness Integrated Competency Model to map the three learning outcomes from the interviews’ analysis: abilities, knowledge, and attitudes. The mapping demonstrated that simulation games could support the development of specific skills and attitudes needed by the information systems sector. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.

Integrating steady-state and dynamic gene expression data for improving genetic network modelling

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

  • Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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  • Description: Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling. © 2022 IEEE.

Intelligent techniques for prediction of drilling rate for percussive drills in topically weathered limestone

- Bhatawdekar, Ramesh, Roy, Bishwajit, Changtham, Saksarid, Khandelwal, Manoj, Armaghani, Danial, Mohamad, Edy, Pathak, Pranjal, Mondal, Subhrojit, Kumar, Radhikesh, Azlan, Mohd

  • Authors: Bhatawdekar, Ramesh , Roy, Bishwajit , Changtham, Saksarid , Khandelwal, Manoj , Armaghani, Danial , Mohamad, Edy , Pathak, Pranjal , Mondal, Subhrojit , Kumar, Radhikesh , Azlan, Mohd
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 457-471
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  • Description: Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weathering. Weathering of limestone takes place very rapidly in tropical (wet) climatic regions. Previous researchers have correlated different individual rock mass properties with rate of drilling. However, single property of limestone is not adequate to correlate with the drilling rate. In this study, sensitivity analysis of different properties of weathered limestone was carried out with respect to drilling rate. Rock density, rock quality designation (RQD), geological strength index (GSI), point load index (PLI) and Schmidt hammer rebound number (SHRN) were identified as crucial input parameters. 113 data sets were collected with the foregoing five input parameters and the output parameter as drilling rate of percussive drills. Data was analysed with multi variable regression analysis (MVRA) which showed R2 value as 0.54. Artificial neural network (ANN) has been widely used for solving various engineering problems. On the other hand, optimization problems are solved by the Biogeography Based Optimization (BBO) model. Further this data was analysed with a hybrid intelligent model namely BBO- ANN. The R2 values for training data set and testing data set 0.638 and 0.761 respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

International workshop on data-driven science of science

- Bu, Yi, Liu, Meijun, Zhai, Yujia, Ding, Ying, Xia, Feng, Acuña, Daniel, Zhang, Yi

  • Authors: Bu, Yi , Liu, Meijun , Zhai, Yujia , Ding, Ying , Xia, Feng , Acuña, Daniel , Zhang, Yi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, USA, 14-18 August 2022, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining p. 4856-4857
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  • Description: Citation data, along with other bibliographic datasets, have long been adopted by the knowledge and data discovery community as an important direction for presenting the validity and effectiveness of proposed algorithms and strategies. Many top computer scientists are also excellent researchers in the science of science. The purpose of this workshop is to bridge the two communities (i.e., the knowledge discovery community and the science of science community) together as the scholarly activities become salient web and social activities that start to generate a ripple effect on broader knowledge discovery communities. This workshop will showcase the current data-driven science of science research by highlighting several studies and constructing a community of researchers to explore questions critical to the future of data-driven science of science, especially a community of data-driven science of science in Data Science so as to facilitate collaboration and inspire innovation. Through discussion on emerging and critical topics in the science of science, this workshop aims to help generate effective solutions for addressing environmental, societal, and technological problems in the scientific community. © 2022 Owner/Author.

Issues and challenges with information systems for asset management, maintenance and reliability

- Chattopadhyay, Gopinath, Menta Venkata, Hari, Oseni, Taiwo, Reddy, Venkat, Singh, Amar, Kandra, Harpreet

  • Authors: Chattopadhyay, Gopinath , Menta Venkata, Hari , Oseni, Taiwo , Reddy, Venkat , Singh, Amar , Kandra, Harpreet
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
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  • Description: Technological changes have been happening in production facilities and infrastructure industries in an ever-increasing rate for asset management. maintenance and reliability. The use of an Asset Management System (AMS) in line with ISO55000 as a documented system and supported by information system is a long- term journey and development efforts will continue to be an essential part of its utilisation for retaining value of and realising value from assets. It is essential to regularly update information, systems and technologies to meet operational, legal, and regulatory requirements (Hastings, 2021). Data integrity is important factor in AMS. While changes are inevitable, the use of AMS must incorporate both the need to make changes, and the ability to exercise control over the validity of changes (Hastings, 2021). A management team that meticulously cares about its assets and incorporates a proactive culture by demonstrating leadership and providing necessary support for excellence to asset management supported by data driven risk informed decisions through effective utilisation of information systems is key to success of any organisation. Good practices in data collection, recording and analysis using user-friendly information system, capability and motivation are essential for risk informed and value driven decision making through systems, tools and technologies. This helps in moving to higher levels of asset management maturity and utilisation of information system for reducing costs, risks and enhancing performance. The scope of this article is limited to some sectors, their issues and challenges and related needs in this area from user perspective. It covers literature review along with reflections from industries on the current gaps, lessons learned and opportunities for improvements. © 2022 IEEE.

Layered malicious nodes detection with graph attention network in human-cyber-physical networks

- Lin, Yuhang, Huang, Yanze, Hsieh, Sun-Yuan, Lin, Limei, Xia, Feng

Learning, Un-Learning, and Relearning in 4IR in Rural Environments

- Kishore, N, Pretorius, Jan, Chattopadhyay, Gopi

  • Authors: Kishore, N , Pretorius, Jan , Chattopadhyay, Gopi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
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  • Description: The Fourth Industrial Revolution (4IR) requires constant learning by people and machines to belong. Every person and all devices have become a node (i.e., attached to a network) [1] to "exist."The relevance of possessing the most important literacies and skills has put pressure on people from all ages and walks of life. For humans to obtain knowledge (technology, data, and human literacy) in 4IR, there will be a new generation of knowledge acquisition in the different stages of the human cycle due to radical industrial change. The need for 21st-century children to incorporate 4IR in their learning is to improve STEM (Science, Technology, Engineering, and Mathematics) performance and critical thinking ability. The learning phase for children will create more logical and analytical adults that can co-exist with robots, cobots (collaborative robots), and AI (Artificial Intelligence). This form of learning is achieved through teaching children coding and robotics at schools from early childhood development (ECD) up until adulthood [2]. However, the critical condition of acquiring the set of skills is having access to them frequently. In rural areas, the basics for humanity to exist is the first battle that is fought daily before the children can even think of education. Hence, there are two battles kids in South Africa (especially the rural area) must overcome. However, once the children have access to education, learning 4IR will be the only type of knowledge they know. The same first-time learning of 4IR cannot be applied to adults with skills acquired previously through formal qualifications and trade experience, as one needs to thrive in the digital economy and new trends [3]. One must become a lifelong learner. Hence, the significant challenges people who live in Rural areas must overcome are their daily challenges while competing to stay ahead of the latest industrial revolution. This gives birth to the new norm of unlearning the previous survival techniques and re-learning how to co-exist in the digital machine world. The article shows the impact of 4IR and asset management interventions in the health sector in rural environments, enabling professionals to learn, unlearn, and relearn. The introduction of a new Health System using 4IR shares the benefits to healthcare workers and patients. © 2022 IEEE.

Machine learning-based agoraphilic navigation algorithm

- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, 17-20 October 2022, Proceedings IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Vol. 2022-October
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  • Description: This paper presents a novel machine learning-based Agoraphilic (free space attraction) navigation algorithm. The proposed algorithm is capable of undertaking local path planning for mobile robots in unknown dynamic environments with a moving goal. The inability to track and reach a moving goal is one of the common weaknesses of most existing navigation algorithms operating in dynamic environments. High uncertainty involved in dynamic environments is also another major challenge. The novel machine learning-based approach helps the proposed algorithm to successfully overcome these challenges. This paper also introduces the integrated modular-based architecture for free-space attraction-based algorithms. This allows the algorithm to incorporate ten different modules with miscellaneous algorithms to perform sub-tasks such as tracking, prediction, map generation, machine learning-based free space attraction force generation and robot motion command generation. The new modular-based architecture integrates those sub-modules to create the robot's driving force. This driving force is the single attractive force to pull the robot towards the moving goal via current free space leading to future free space passages. The proposed algorithm was experimentally tested under a dynamic environment. The experiment was focused on testing the behaviour of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that the Agoraphilic algorithm is successful in reaching a moving goal in an unknown dynamically cluttered environment. © 2022 IEEE.

Mechanical performance of tire-derived aggregate permeable pavements under live traffic loads

- Raeesi, Ramin, Soltani, Amin, King, Russell, Disfani, Mahdi

  • Authors: Raeesi, Ramin , Soltani, Amin , King, Russell , Disfani, Mahdi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th International Conference on Transportation Geotechnics, ICTG 2021 Vol. 164, p. 515-528
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  • Description: Traditional pavements in urban areas are mainly rigid, impervious surfaces, resulting in augmented surface run off during rainfalls, thereby leading to flash-flooding and pollution of waterways. In comparison, permeable pavements permit percolation of water through surface layers, thus alleviating harmful environmental impacts. This study presents the authors’ recent experience in the development of an instrumented large-scale permeable pavement trial site—constructed using a combination of crushed rock (CR) and tire-derived aggregate (TDA), bonded by a polyurethane (PU) binder—located at a car park in South Australia. An area of approximately 400 m2 was paved using different TDA-based mix designs—different CR sizes, colors, and shapes, and different PU contents. Moreover, preliminary field performance monitoring results—including surface deflection measurements by the light weight deflectometer test, and strain measurements using optic fiber and strain gauge sensing techniques—are outlined and discussed in detail. The strength and stiffness of the TDA-based blend was found to be dependent on the TDA content, TDA-to-CR size ratio, CR shape, and PU content. The greater the angularity of the CR particles, the more effective the interlocking of the TDA and CR components, and thus the higher the developed strength and stiffness. The amount of strain generated in the permeable surface layer and at its interface with the lower screening layer were both found to decrease with a decrease in TDA content. Preliminary field observations indicate that the TDA-based system can indeed be a viable (and sustainable) solution to effectively mitigate flash-flooding while sustaining low–medium traffic loads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

MET-Meme : a multimodal meme dataset rich in metaphors

- Xu, Bo, Li, Tingtin, Zheng, Junzhe, Naseriparsa, Mehdi, Zhao, Zhehuan, Lin, Hongfei, Xia, Feng

Metric learning-based few-shot malicious node detection for IoT backhaul/fronthaul networks

- Zhou, Ke, Lin, Xi, Wu, Jun, Bashir, Ali, Li, Jianhua, Imran, Muhammad

  • Authors: Zhou, Ke , Lin, Xi , Wu, Jun , Bashir, Ali , Li, Jianhua , Imran, Muhammad
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 5777-5782
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  • Description: The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node. © 2022 IEEE.

Modelling and experimental assessment of high-frequency oscillation in DC microgrid

- Habibullah, Mohammad, Shah, Rakibuzzaman, Mithulananthan, Nadarajah, Islam, Syed, Islam, Rabiul

Neighbourhood houses and men’s sheds, adult learning in community spaces. Transforming the future of education : the role of research

- Smith, Erica, Ollis, Tracey

Overview of business hyper-automation

- Zhao, Xiaohui, Oseni, Taiwo, Medishetty, Bhanu

Physics-informed graph learning

- Peng, Ciyuan, Xia, Feng, Saikrishna, Vidya, Liu, Huan

Power quality improvement of a solar powered bidirectional smart grid and electric vehicle integration system

- Paidimukkala, Nikitha, Das, Narottam, Islam, Syed

  • Authors: Paidimukkala, Nikitha , Das, Narottam , Islam, Syed
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th IEEE Sustainable Power and Energy Conference, iSPEC 2022, Virtual, online, 4-7 December 2022, Proceedings - 2022 IEEE Sustainable Power and Energy Conference, iSPEC 2022
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  • Description: This research paper mainly focuses on photovoltaic (PV) smart grid (SG) and electric vehicles (EVs) integration with two-way power flow capabilities by charging/discharging and power quality improvements using the power converters. Due to the increase of energy demand by rapid growth of population, it is necessary to modernize the power grid with improved power quality, such that the energy converted by solar PV (SPV) can be transmitted and stored as the excess amount of power in terms of batteries to use at peak load demand. The batteries of the EVs charge at a low level of demand and discharged at peak demand. The EVs can function both as a load and an energy supplier to the SG. The simulation results demonstrate the functioning of interfaced smart G2V system by observing and improving the factors such as power factor, power regulation and elimination of harmonics by constructing a power electronic network which can perform bidirectional power flow and balancing the network using MATLAB/Simulink software. Subsequentially, the improvement of power quality of the integrated system by analyzing power compensation, voltage regulation and harmonics mitigation of the integration system has been examined in detail. © 2022 IEEE.

Prediction of positive cloud-to-ground lightning striking zones for tilted thundercloud based on line charge model

- Barman, Surajit, Shah, Rakibuzzaman, Islam, Syed, Kumar, Apurv

  • Authors: Barman, Surajit , Shah, Rakibuzzaman , Islam, Syed , Kumar, Apurv
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 14th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022, Melbourne, 20-23 November 2023, 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC) Vol. 2022-November
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  • Description: Bushfire is known as one of the ascendant factors to create pyrocumulus thundercloud that causes the ignition of new fires by pyrocumulonimbus (pyroCb) lightning strikes mostly of positive polarity, and causes massive damage to nature and infrastructure. A conceptual model-based risk planning would be beneficial to predict the lightning striking zones on the surface of the earth underneath the pyroCb thundercloud. In this paper, a simple line charge structured thundercloud model is constructed in 2-D coordinates using the method of images to predict the probable +CG (positive cloud-To-ground) lightning striking zones on the earth's surface for tilted dipole thundercloud charge configuration. The electric potential distribution and ground surface charge density for tilted dipole thundercloud is investigated via continuously adjusting the position and charge density of its charge regions. Simulation results confirm the initiation of negative charged density for the wind shear extension of upper positive charge region by 2 to 8 km, and would expect +CG lightning to strike within 7.88 to 20 km around the earth periphery particularly in the direction of the cloud's forward flank. The proposed model would serve as the foundation to identify the probable lightning affected area as well as can also be extended to analyze the hazardous situation appears in wind energy farms or agricultural fencing situated nearby the power grid during pyroCb events. © 2022 IEEE.

Prediction of rockburst using supervised machine learning

- Kishore, Tharun, Khandelwal, Manoj

  • Authors: Kishore, Tharun , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 133-154
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  • Description: The rockburst is one of the serious mining hazards, that cause injury, death, damage to mining equipment, and leads to financial problems in mining constructions. At the same time, mining is the major and essential resource of mineral commodities, that all mining locations uncover important for sustaining and enhancing their needs of dwelling. Moreover, mining contributes a significant portion of the GDP (Gross Domestic Production) growth of nations such as China, Australia, Russia, and the USA. These countries play major roles in exporting mineral commodities, which has been creating a huge demand to find out conventional methods for predicting the rockburst occurrence during mining. Moreover, determining rockburst occurrence using formulation or machine-based equipment has not been ideal in showcasing the result, due to changing geological parameters and the mining environment. In recent years, various soft computing and machine learning tools have been developed significantly, which help us to improve predicting models more accurate. In this research, original Rockburst data set have been collected in the last 20 years. Then standard empirical formulation method which is currently used in mining and supervised machine learning models has been used to predict rockburst occurrence. Finally, an interacting machine learning model developed in a way to predict the Rockburst occurrence result in three Indicators (high, medium and low Rockburst) are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prioritization of clinical alarms using semantic features of vital signs in remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Mai, Shenhan

  • Authors: Arora, Teena , Balasubramanian, Venki , Mai, Shenhan
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 242-245
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  • Description: In recent years remote patient monitoring applications have emerged that can monitor the patient continuously and remotely with the help of wearable sensors that collect physiological data and send it to a telemedicine platform. Sensitivity of the sensor, patient's movement, electromagnetic interference, and data processing algorithms are a few factors that affect the collected data, leading to false alarms, and consequent false alarm leads to alarm fatigue. This study presents novel factors such as trust, frequency, slope, and trend that transform the vital signs raw data from the sensors into semantic data in a remote monitoring application. Experimental results have shown that data transformations lead to a reduction in clinically non-significant alarms and the prioritization of clinically significant alarms. © 2022 ACM.

Psychoinformatics : the behavioral analytics

- Nimje, Sparsh, Katade, Jayesh, Dunbray, Nachiket, Mavale, Shreyas, Kulkarni, Siddhivinayak, Firmin, Sally

  • Authors: Nimje, Sparsh , Katade, Jayesh , Dunbray, Nachiket , Mavale, Shreyas , Kulkarni, Siddhivinayak , Firmin, Sally
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 3rd International Conference on Communication, Computing and Electronics Systems, ICCCES 2021, Coimbatore, India, 28-29 October 2021, Proceedings of Third International Conference on Communication, Computing and Electronics Systems Vol. 844, p. 547-562
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  • Description: Human behavior is very complex and cannot be explained using traditional mathematical models. Intermediate forms, such as those obtained from personality data, can be used to predict behavioral aspects of a person, creating the hypothesis that arbitrating psychological models can be drawn directly from recordings of behavior. In recent years, smartphone addiction has increased to a great extent. Since the excessive use of smartphones has negatively affected our daily life, many applications to reduce dependence on smartphones have been developed around the world. Personal attributes or personality types can be extracted from data obtained directly from smart phones without the interaction of participants who may have social or health interventions. Many people who excessively use their smartphones have an uncontrollable urge to use the Internet. Internet addiction refers to uncontrolled use of the Internet which causes hindrance in our daily life. Due to its negative impact on the education and lives of people, it is necessary to detect tendencies of people toward addictive behavior and provide them with preventative support and treatment. Similarly, the development of social media has seen rapid growth in its usage. People often find themselves overusing utilities such as virtual communication, texting, and sharing information which have also caused various behavioral problems. This study provides a summary of the various methods and studies done on these behavioral problems and to analyze different techniques, and machine learning models are used to predict addictive personality types. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.