https://cyberleninka.org/article/n/854933.pdf

Social media analytics – Challenges in topic discovery, data collection, and data preparation

In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a computer-added detection (CAD) system optimized with "average" masses with another CAD system optimized with "subtle" masses. The two single CAD systems have similar image processing steps, which include prescreening, object segmentation, morphological and texture feature extraction, and false positive (FP) reduction by rule-based and linear discriminant analysis (LDA) classifiers. A feed-forward backpropagation artificial neural network was trained to merge the scores from the LDA classifiers in the two single CAD systems and differentiate true masses from normal tissue. For an unknown test mammogram, the two single CAD systems are applied to the image in parallel to detect suspicious objects. A total of three data sets were used for training and testing the systems. The first data set of 230 current mammograms, referred to as the average mass set, was collected from 115 patients. We also collected 264 mammograms, referred to as the subtle mass set, which were one to two years prior to the current exam from these patients. Both the average and the subtle mass sets were partitioned into two independent data sets in a cross validation training and testing scheme. A third data set containing 65 cases with 260 normal mammograms was used to estimate the FP marker rates during testing. When the single CAD system trained on the average mass set was applied to the test set with average masses, the FP marker rates were 2.2, 1.8, and 1.5 per image at the case-based sensitivities of 90%, 85%, and 80%, respectively. With the dual CAD system, the FP marker rates were reduced to 1.2, 0.9, and 0.7 per image, respectively, at the same case-based sensitivities. Statistically significant (p < 0.05) improvements on the free response receiver operating characteristic curves were observed when the dual system and the single system were compared using the test sets with either average masses or subtle masses.

Dual system approach to computerâ aided detection of breast masses on mammograms

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Deep learning for misinformation detection on online social networks: a survey and new perspectives

Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research.

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Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Detecting hot social events (e.g., political scandal, momentous meetings, natural hazards, etc.) from social messages is crucial as it highlights significant happenings to help people understand the real world. On account of the streaming nature of social messages, incremental social event detection models in acquiring, preserving, and updating messages over time have attracted great attention. However, the challenge is that the existing event detection methods towards streaming social messages are generally confronted with ambiguous events features, dispersive text contents, and multiple languages, and hence result in low accuracy and generalization ability. In this paper, we present a novel reinForced, incremental and cross-lingual social Event detection architecture, namely FinEvent, from streaming social messages. Concretely, we first model social messages into heterogeneous graphs integrating both rich meta-semantics and diverse meta-relations, and convert them to weighted multi-relational message graphs. Second, we propose a new reinforced weighted multi-relational graph neural network framework by using a Multi-agent Reinforcement Learning algorithm to select optimal aggregation thresholds across different relations/edges to learn social message embeddings. To solve the long-tail problem in social event detection, a balanced sampling strategy guided Contrastive Learning mechanism is designed for incremental social message representation learning. Third, a new Deep Reinforcement Learning guided density-based spatial clustering model is designed to select the optimal minimum number of samples required to form a cluster and optimal minimum distance between two clusters in social event detection tasks. Finally, we implement incremental social message representation learning based on knowledge preservation on the graph neural network and achieve the transferring cross-lingual social event detection. We conduct extensive experiments to evaluate the FinEvent on Twitter streams, demonstrating a significant and consistent improvement in model quality with 14%–118%, 8%–170%, and 2%–21% increases in performance on offline, online, and cross-lingual social event detection tasks.

Reinforced, Incremental and Cross-Lingual Event Detection From Social Messages

The online social networks (OSNs) have become an important platform for detecting real-world event in recent years. These real-world events are detected by analyzing huge social-stream data available on different OSN platforms. Event detection has become significant because it contains substantial information which describes different scenarios during events or crisis. This information further helps to enable contextual decision making, regarding the event location, content and the temporal specifications. Several studies exist, which offers plethora of frameworks and tools for detecting and analyzing events used for applications like crisis management, monitoring and predicting events in different OSN platforms. In this paper, a survey is done for event detection techniques in OSN based on social text streams—newswire, web forums, emails, blogs and microblogs, for natural disasters, trending or emerging topics and public opinion-based events. The work done and the open problems are explicitly mentioned for each social stream. Further, this paper elucidates the list of event detection tools available for the researchers.

A survey of event detection techniques in online social networks

Developing widely useful mobile computing applications presents difficult challenges. On one hand, mobile users demand intuitive user interfaces, fast response times, and deep relevant content. On the other hand, mobile devices have limited processing, storage, power, display, and communication resources. Caching frequently accessed data items on the mobile client is an effective technique to improve the system performance in mobile environment. Due to cache size limitation, the choice of cache replacement technique to find a suitable subset of items for eviction from cache becomes important. In this paper, we propose a new cache replacement policy for location dependent data in mobile environment. The proposed policy selects the predicted region based on client's movement and uses it to calculate the weighted data distance of an item. This makes the policy adaptive to client's movement pattern and provides importance to the regions around client's position. This is unlike earlier policies that consider the directional/non-directional data distance only. We call our policy the Weighted Predicted Region based Cache Replacement Policy (WPRRP). Simulation results show that the proposed policy significantly improves the system performance in comparison to previous schemes in terms of cache hit ratio.

A weighted cache replacement policy for location dependent data in mobile environments

Caching frequently accessed data items on the mobile client is an effective technique to improve the system performance in mobile environment. Proper choice of cache replacement technique to find a suitable subset of items for eviction from cache is very important because of limited cache size. Available policies do not take into account the movement patterns of the client. In this paper, we propose a new cache replacement policy for location dependent data in mobile environment. The proposed policy uses a predicted region based cost function to select an item for eviction from cache. The policy selects the predicted region based on client’s movement and uses it to calculate the data distance of an item. This makes the policy adaptive to client’s movement pattern unlike earlier policies that consider the directional / non-directional data distance only. We call our policy the Prioritized Predicted Region based Cache Replacement Policy (PPRRP). Simulation results show that the proposed policy significantly improves the system performance in comparison to previous schemes in terms of cache hit ratio.

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A Predicted Region based Cache Replacement Policy for Location Dependent Data in Mobile Environment

Infection with parasitic copepod salmon louse Lepeophtheirus salmonis, represents one of the most important limitations to sustainable Atlantic salmon (Salmo salar L.) farming today in the North Atlantic region. The parasite exerts negative impact on health, growth and welfare of farmed fish as well as impact on wild salmonid populations. It is therefore central to ensure continuous low level of salmon lice with the least possible handling of the salmon and drug use. To address this, vaccination is a cost-effective and environmentally friendly control approach. In this study, efficacy of a vaccine candidate, containing a peptide derived from ribosomal protein P0, was validated post infestation with L. salmonis, at the lab-scale. The sampling results showed good potential of the vaccine candidate when administered intraperitoneally in the host, in reducing the ectoparasite load, through reduction of adult female lice counts and fecundity and with greater presumptive effect in F1 lice generation. The sampling results correlated well with the differential modulation of pro-inflammatory, Th1, Th2 and T regulatory mediators at the transcript level at different lice stages. Overall, the results supports approximately 56% efficacy when administered by intraperitoneal injection. However, additional validation is necessary under large-scale laboratory trial for further application under field conditions.

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Impact of a candidate vaccine on the dynamics of salmon lice (Lepeophtheirus salmonis) infestation and immune response in Atlantic salmon (Salmo salar L.)

The performance of wireless sensor networks (WSNs) can be significantly improved using adaptive cooperative diversity (SCA) technique, which is basically a cross-layer design that combines truncated ARQ at the link layer and cooperative diversity at the physical layer. LEACH is a protocol for wireless sensor networks that utilizes randomized rotation of local cluster heads to evenly distribute the energy load among the sensors in the network. This paper proposes and evaluates the performance of a selective cooperative scheme for wireless sensor networks based on scalability. The evaluation of the lifetime and packet loss rate is presented and results based on the maximum number of retransmissions, network size, packet length and initial energy by nodes are provided. Our scheme shows better performance as compared to others.

Ajey Kumar

Papers

A survey of event detection techniques in online social networks

A weighted cache replacement policy for location dependent data in mobile environments

A Predicted Region based Cache Replacement Policy for Location Dependent Data in Mobile Environment

Impact of a candidate vaccine on the dynamics of salmon lice (Lepeophtheirus salmonis) infestation and immune response in Atlantic salmon (Salmo salar L.)

Scalability in an adaptive cooperative system for wireless sensor networks