Kinds Of Images Research Articles

Future school well-being: a qualitative study on the imagined futures of Finnish youth

ABSTRACT Supporting the well-being of young people is one of a school’s key goals. We examined school well-being from the perspective of young people by asking them what kinds of images they had of school well-being in future schools. Using a qualitative research design to obtain an open and interpretive response, we analysed essays written by Finnish general upper secondary school students (n = 89). With an analysis that utilised Dator’s (2009. ‘Alternative futures at the Manoa School.’ Journal of Futures Studies 14 (2): 1–18) archetypes of future images, Allardt’s (1996. ‘Hyvinvointitutkimus ja elämänpolitiikka’ [Welfare research and life policy]. Janus 4 (3): 224–241) sociological model of well-being, and components of Konu and Rimpelä’s (2002. ‘Well-being in schools: a conceptual model).’ Health Promotion International 17 (1): 79–87. doi:10.1093/heapro/17.1.79) school well-being model, we present a versatile perspective on school well-being via four alternative future images. The students conveyed desirable and threatening factors at both societal and individual levels, revealing tensions in the young people’s views and experiences regarding their acceptance or their criticism of the current neoliberal educational policies, with their emphasis on individual responsibility, efficiency, and competition. This study contributes to the literature on young people’s school well-being by using a future-oriented approach that involved describing alternative futures. This enables us to explore our understanding of social change and to examine possibilities for future directions in schooling.

DM-AHR: A Self-Supervised Conditional Diffusion Model for AI-Generated Hairless Imaging for Enhanced Skin Diagnosis Applications.

Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM-AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM-AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM-AHR holds considerable promise for broader application in medical image enhancement.

Subject review: White balance techniques of digital image

Due to un control environment during creating of the digital image make the resulting image have un real color cast, for that a white balance algorithm has been found to correct the unreal color cast create by non-canonical Lighting sources, In order to achieve satisfactory color of the digital image, a white balance required to adjust the image color either automatically or manually. for that researchers found an automatic and manual white balance algorithms, and even the Automatic white balance algorithm depending on analysis either low level features or high Level features, explaining An examples for Automatic white balance algorithm ( Gray World Algorithm and The white patch Algorithm ) and clarify each Algorithm suits different kind of Image.

"What do I say?" Reflections on the International Communication Strategy of Chinese Culture on Social Media Platforms

In the Internet era, the social media platform has been an important channel for international people to get information based on the digital technology with rapid development. The information on social media with rapid transmission is short and brief and the presentation is fragmented and simple, providing the public with a platform to conduct exchanges on equal footing, which has given the content a new interpretation. Thus as Chinese culture goes international, social media platforms have also been an important part of China's digital culture industry. Then what kind of image we should build to promote the transmission of Chinese culture to the world and how to present the content of the external communication platform to achieve the emotional resonance of cultural communication between different countries are extremely essential. On the basis of "acculturation theory", the thesis intends to explore and analyze the significant differences in the international dissemination of Chinese culture strategy, determine "what I want to say" under the premise of knowing “who is the information aimed at” and how to make the most of social media platforms to promote acculturation and cross-cultural communication, and propose corresponding an optimal path.

Disassembling the components of virtual clothing presentation: exploring their impact on consumers' perception and purchase intention

PurposeVirtual clothing presentation has a visual 3D effort and allows consumers to interact. Currently, most scholars make virtual clothing presentations as a whole or focus on one element to study its impact on consumers’ purchase intentions. This study disassembles the components of virtual clothing presentation in Taobao and explores how each component (virtual clothing, virtual model, presentation space, presentation technology and system quality) affects consumers’ purchase intention, which can provide clothing companies recommendations for developing it and help improve consumers’ online shopping experience.Design/methodology/approachThis study created the parts of a virtual clothing presentation and took advantage of Taobao’s 3D interactive module to simulate a shopping scenario. Participants experienced a 3D interactive virtual clothing presentation in which they could change clothing colors, show actual clothing sizes, try different model poses, switch presentation backgrounds and unfold clothing structure through interactive buttons. Then they were randomly assigned to view two kinds of images and videos of experiment clothing (real images and videos vs virtual ones), respectively. Hypotheses were tested using SEM and applying SmartPLS 4.FindingsThe results demonstrated that five components of virtual clothing presentation have different degrees of influence on consumers’ purchase intention through different consumer perceptions (perceived usefulness, ease of use and enjoyment). It is necessary to consider the design of the virtual clothing presentation in those five dimensions for different needs. In addition, it is feasible to replace images and animated videos of real clothing with virtual ones in Taobao.Originality/valueDifferent types of virtual clothing presentations can lead to various results in terms of diverse impacts. This study disassembles and researches each component of a virtual clothing presentation. Overall, this study could provide guidance and suggestions for clothing companies to develop and design or invest in virtual clothing presentations.

Medical Image Classification using a Many to Many Relation, Multilayered Fuzzy Systems and AI

One of the research gaps in the medical sciences is the study of orphan diseases or rare diseases, due to limited data availability of rare diseases. Our previous study addressed this successfully by developing an Artificial Intelligence (AI)-based medical image classification method using a multilayer fuzzy approach (MFA), for detecting and classifying image abnormalities for large and very small datasets. A fuzzy system is an AI system used to handle imprecise data. There are more than three types of fuzziness in any image data set: 1) due to a projection of a 3D object on a 2D surface, 2) due to the digitalization of the scan, and 3) conversion of the digital image to grayscale, and more. Thus, this was referred to in the previous study as a multilayer fuzzy system, since fuzziness arises from multiple sources. The method used in MFA involves comparing normal images containing abnormalities with the same kind of image without abnormalities, yielding a similarity measure percentage that, when subtracted from a hundred, reveals the abnormality. However, relying on a single standard image in the MFA reduces efficiency, since images vary in contrast, lighting, and patient demographics, impacting similarity percentages. To mitigate this, the current study focused on developing a more robust medical image classification method than MFA, using a many-to-many relation and a multilayer fuzzy approach (MCM) that employs multiple diverse standard images to compare with the abnormal image. For each abnormal image, the average similarity was calculated across multiple normal images, addressing issues encountered with MFA, and enhancing versatility. In this study, an AI-based method of image analysis automation that utilizes fuzzy systems was applied to a cancer data set for the first time. MCM proved to be highly efficient in detecting the abnormality in all types of images and sample sizes and surpassed the gold standard, the convolutional neural network (CNN), in detecting the abnormality in images from a very small data set. Moreover, MCM detects and classifies abnormality without any training, validation, or testing steps for large and small data sets. Hence, MCM may be used to address one of the research gaps in medicine, which detects, quantifies, and classifies images related to rare diseases with small data sets. This has the potential to assist a physician with early detection, diagnosis, monitoring, and treatment planning of several diseases, especially rare diseases.

Shifting spatial metaphors in Russia at a time of war

ABSTRACT Russia’s war with Ukraine is reshaping Russia’s geopolitical orientation and transforming the ways the country relates to its immediate neighbours. In this article, rather than undertaking a geopolitical analysis of the consequences inside Russia of the ongoing war in Ukraine, we look at public discourse as one aspect of political activity. We describe how processes of a holistic kind are depicted in metaphors and how new spatial metaphors are emerging. The war has pushed earlier somatic and emotion-laden state-generated images into coexistence with new tropes of centripetal convergence around the President and loyalty to the power vertical. We also bring to attention other kinds of images: the downbeat metaphors and historical analogies with which people describe Russia’s changing actuality, and the new spatial-territorial images produced by ethnic groups who are looking at Russia from alternative, non-central and sometimes conflictual perspectives. Unlike the national homogenisation that has developed from Xi Jinping’s image of ‘China’s dream’, Russia remains a consciously ‘imperial’, multiethnic and economically diverse country. We suggest that examining the influential metaphors by which it is imagined, both the holistic and the diverse, incongruous or splintered, is one way to capture the multiplicity of a country at war and in flux.

The image of nursing in the eyes of Generation Alpha and their parents in Türkiye: A multicenter cross-sectional survey

Background and purposeParents' views of nursing may play a key role in what kind of image their children hold of it. This study had three objectives. First, it investigated the image of nursing in the eyes of Generation Alpha and their parents. Second, it addressed whether their views affected each other. Third, it investigated factors that might affect their views. MethodsThis multicenter study adopted a descriptive correlational and cross-sectional design. The sample consisted of 1742 children and their parents (n = 3484). Data were collected using a demographic characteristics questionnaire and the Nursing Image Scale (NIS). The data were analyzed using Pearson correlation and stepwise regression analysis. ResultsChildren, mothers, and fathers had a total NIS score of 48.76 (7.57), 42.37 (8.04), and 43.22 (8.99), respectively. Mothers' and fathers' NIS scores were positively associated with children's NIS scores (R2 = 0.286; 0.365, p < .01). Children's and their parents' negative experiences with nurses were negatively associated with their NIS scores (R2 = 0.358; 0.191; 0.155, p < .01). Children described nurses as “coolies,” “maids,” “machines,” “devils,” or “white ghosts.” Their metaphors were negatively associated with their NIS scores. Almost half the participants stated that nursing was portrayed negatively in the media. ConclusionChildren have a more positive view of nursing, but their views are also affected by their parents' thoughts. Practice implicationsNurses should be professionally autonomous, promote their professional identity through the media, and interact with their colleagues to have a professional image in the eyes of children and their parents.

RWHO: A hybrid of CNN architecture and optimization algorithm to predict basal cell carcinoma skin cancer in dermoscopic images

Basal cell carcinoma (BCC) is a type of skin cancer that initiates from the epithelial cells of our skin. Compared to other forms of cancer, BCC infrequently spreads to other parts of the body. It has a risk of local attack and demolition of surrounding tissues. Typically, BCC shows as one or numerous small, glowing nodules exhibiting central depressions. These knots are commonly found on the sun-exposed skin areas of older adults. Many dermatoscopic methods are available for diagnosing and predicting such kinds of skin cancers. But, medical professionals find it difficult to diagnose at some kind of images at the early stages. An automated methodology to predict such types of skin lesions would be better for such a diagnosis. In the present work, a new computer-assisted algorithm called RESNET50-WHO (RWHO) has been introduced to predict and diagnose BCC skin cancer. The method uses a combination of deep learning algorithm RESNET 50 and a metaheuristic algorithm, called wildebeest herd optimization (WHO) Algorithm to do prediction. The initial features from the images are extracted using RESNET 50. The output is given to the WHO algorithm to extract the beneficial features to reduce the time complexity. The method is tested using the PH2 dataset. The results obtained using the proposed algorithm is compared with the state-of-art optimization algorithms and evaluated. The conclusive findings specify that the proposed algorithm beats the comparative methods, yielding superior results

Response, News, and Observation: Typologies of Communication in VR Documentaries

The combination of virtual reality (VR) technology and documentary style creates VR documentaries with distinctive image characteristics. This kind of image with its special spatial interaction, sense of presence, and immersion counters the one-way cognitive thinking of traditional documentary and reshapes its media properties with VR technology, which in turn generates new perception, identity, emotional experience, and understanding in the context of virtual immersive documentary image dissemination.

Radiologic Evaluation of Uncinate Processes of the Cervical Spine and the Relationship Between the Uncinate Process and Vertebral Artery: Implication in Anterior Cervical Spine Surgery

To determine the relationship between the uncinate process (UP) and vertebral artery (VA) from a radiologic view and to confirm the surgical safety margin to minimize the risk of VA injury during anterior cervical approaches. We retrospectively reviewed computed tomography angiography of 205 patients by using a contrast-enhanced computed tomography angiography protocol of the VA. Four kinds of images were simultaneously reconstructed to measure all the parameters associated with VA and UP of cervical spine. The shortest distance from the UP's tip to the VA's medial border (P < 0.001) was at the C-6 level (2.9 ± 0.9 mm on the left and 3.2 ± 1.3 mm on the right), and the longest distance (P < 0.001) was at the C-3 level on both sides. The distance between UP's tip and the medial border of the ipsilateral VA was statistically significantly different at each cervical level, and the right distance was larger than the left (P < 0.05). We found the height of UP gradually increased from C-3 to C5-level and then decreased from C-5 to C-7 level for both sides. The mean distance between the medial borders of left UP and left VA was on average 7.5 ± 1.4 mm. The diameter of VA was on average 3.4 ± 0.6 mm on the left side and 3.2 ± 0.7 mm on the right. The diameter of the VA was statistically significantly different on both sides, and the left side was larger than the right (P < 0.05). Detailed radiologic anatomy of VA and UP was reviewed in this study. A deep understanding of the correlation between the UP and VA is essential to perform anterior cervical spine surgery safely and ensure adequate spinal canal decompression.

‘[M]en’s Dwellings Were Thin Shells’: Uncertain Interiors and Domestic Violence in Ford Madox Ford’s War Writing

The standard image of First World War soldiers is of men in open trenches: waiting to attack or be attacked; walking, sitting, sleeping, dead. Ford’s Parade’s End includes such scenes. But it is a different kind of image which predominates in his war writings and often produces its most memorable passages: images of houses or house-like shelters. The mind seeks protection in such structures; but they offer little security against the destructiveness outside, against the bombardments, gas, shrapnel, bullets. Ford wrote that the experience of war revealed: ‘men’s dwellings were thin shells that could be crushed as walnuts are crushed. … all things that lived and moved and had volition and life might at any moment be resolved into a scarlet viscosity seeping into the earth of torn fields […]’. This realisation works in two ways. The soldier’s sense of vulnerability provokes fantasies of home, solidity, sanctuary, while for the returnee soldier, domestic architecture summons war-visions of its own annihilation: ‘it had been revealed to you’, adds Ford, ‘that beneath Ordered Life itself was stretched, the merest film with, beneath it, the abysses of Chaos’. It is now customary to read war literature through trauma theory. Building on analyses of Ford’s use of repression, but drawing instead on object relations theory, I argue that Ford’s houses of war are not screen memories but images of the failure of repression to screen off devastating experiences. The abysses of Chaos can be seen through the screen or projected upon it. Attending to Ford’s handling of this theme enables a new reading of his war writing and a new case for its coherence. The essay will connect the opening of No More Parades (in a hut, during a bombardment) with the war poem ‘The Old Houses of Flanders’; the postwar poem A House; the memoir It Was the Nightingale (quoted above); and the otherwise puzzling, fictionalised memoir No Enemy, structured in terms of ‘Four Landscapes’ and ‘Certain Interiors’.

Beam-shaped femtosecond laser printing of quasi-capsule-shaped holographic terahertz metasurfaces.

Terahertz (THz) metasurfaces have opened up a new avenue for the THz wavefront modulation. However, high-efficient and low-cost fabrication of THz metasurfaces remains a great challenge today. Here, quasi-capsule-shaped polarization-multiplexed holographic THz metasurfaces were printed by a beam-shaped femtosecond laser. The laser beam was spatially modulated by holograms of optimized cylindrical lens loaded on a spatial light modulator (SLM). The size of quasi-capsule apertures can be exquisitely and flexibly controlled by adjusting the focal length in holograms, pulse energy, and pulse number. Based on near-field diffraction and Burch encoding, an array of 100 × 100 basic unit apertures were initially designed, and a polarization-multiplexed THz metasurface was finally printed with a dimension of 8 mm × 8 mm. The function of polarization multiplexing was demonstrated, by which two kinds of images were reconstructed in response to X and Y-polarization THz waves, respectively. The present work highlights a great leap in fabrication method for THz metasurfaces and hopefully stimulates the development of miniaturized and integrated THz systems.

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

XNET: CNN MODEL for COVID-19 Classification using Skip Connection

The dangerous virus so far has been the new Coronavirus A shocking turn of events has resulted in a catastrophic disease that has swept the globe. This lethal outbreak has sparked worries about the burden it puts on healthcare systems, especially when it comes to serving a sizable population that needs urgent care. RT-PCR RAT has emerged as the two main tests used to meet the urgent demand for diagnostics. These tests are preferred because they can produce results quickly. However, relying only on these two approaches has several drawbacks. The issue is made worse by the fact that many countries struggle to obtain enough testing kits. Further complicating the problem are reports of false-positive results. Chest X-rays are a supplemental tool that can be extremely helpful in reducing these difficulties and stopping the spread of COVID. Healthcare practitioners can get quick findings from RT-PCR testing and chest X-rays, allowing patients to take the required precautions immediately. This multifaceted strategy improves the effectiveness of diagnosis and therapy while easing pressure on testing facilities. Utilizing the benefits of various diagnostic techniques, we can address the current issue more skillfully and protect public health. In order to categorize patients with COVID-19 using chest X-ray (CXR) pictures, A CNN Model is first implemented within this research. The Kaggle-sourced dataset for this study consists of two kinds of images: infected and healthy. Techniques for transfer learning are used to improve the CNN. Model’s functionality and effectiveness. We want to enhance the accuracy and reliability of the classification process specifically for COVID-19 identification in CXR pictures by utilizing existing knowledge and skills from pre-trained models. After applying several transfer learning techniques we face the problem of overfitting. To overcome that overfitting, we used residual blocks (skip-connection). After applying this model, we got an accuracy of 96%. So, this technique will help in the future to avoid all problems of expenses and difficulties that we face in hospitals. Overall, this technique has the potential to enhance the accuracy, speed, and efficiency of Covid-19 classification, making it an important tool in the fight against the pandemic.

A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries

Image segmentation is a complex mathematical problem, especially for images that contain intensity inhomogeneity and tightly packed objects with missing boundaries in between. For instance, Magnetic Resonance (MR) muscle images often contain both issues, making muscle segmentation especially difficult. In this paper we propose a novel intensity correction and a semi-automatic active contour-based segmentation approach. The approach uses a geometric flow that incorporates a Reproducing Kernel Hilbert Space (RKHS) edge detector and a geodesic distance penalty term from a set of markers and anti-markers. We test the proposed scheme on MR muscle segmentation and compare with some state-of-the-art methods. To help deal with the intensity inhomogeneity in this kind of image, a new approach to estimate the bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy C-means (PBCFCM), is introduced. Numerical experiments show that the proposed scheme leads to significantly better results than compared ones. The average dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps, hamstrings and other muscle groups while other approaches are at least 10% worse.

Koreans’ Reception of Japanese Cultural Contents Revealed through Text Mining

This paper analyzes how Koreans perceive Japanese cultural contents and popular culture, and what kind of image they have formed of it. For this purpose, this study used the blog of NAVER, Korea’s largest portal site, as its research subject, and collected and analyzed data from it using text mining. The word frequency and N-gram analysis results of the data collected in this study are as follows. The main keywords in this data are roughly divided into a cluster representing Japanese cultural contents/popular culture, a cluster of keywords representing Koreans’ attitude toward or opportunity to access and encounter it, and a cluster of keywords representing the relationship of Japanese culture with Korean culture. In particular, the salient fact that emerges from these keywords is that the Japanese pop culture openness policy of 1998 has been a very important development for Koreans in terms of their relationship with Japanese cultural contents. In this respect, there is a tendency to think of Japanese cultural contents and popular culture from the perspective of Korea-Japan cultural exchange and the mutual understanding between the two countries. Meanwhile, the results of this study’s Sentiment Analysis showed that there were an overwhelming number of keywords expressing positive emotions in the data on Japanese cultural contents and popular culture.

Mixed gray-color images encryption algorithm based on a memristor chaotic system and 2D compression sensing

Multi-image encryption has attracted great interest in the era of secret communication, however, the mixed encryption of gray and color images has been reported rarely. Meanwhile, the security of image encryption based on chaos system depends heavily on the performance of pseudo randomness and sensibility. Thus, a four-dimensional chaotic system is improved by introducing a memristor, which has good nonlinear characteristic and can enhance chaotic property. It is found that this new system possesses larger chaotic scope and exhibits a coexistence phenomenon, all of which reveal the intricate nature and underlying application. After the verification of spectral entropy and circuit simulations of the new system, a mixed-image encryption algorithm based on two-dimension compression sensing (2DCS) is designed innovatively. To achieve the target of compression and encryption, the image set is first preprocessed and varied utilizing DWT, and then is sampled by measurement matrix in two-dimension. Next, the pixels of compressed image set are diffused by employing DNA coding. The accuracy of reconstruction image is closely related to the randomness and sparsity of the measurement matrix. Chaotic system with pseudo-randomness can be fully exploited to fabric a measurement matrix. Hence the Toeplitz cyclic chaos matrix is conceived and the reconstruction accuracy does have better results. In addition, this method can not only encrypt multiple mixed images with grey and color simultaneously, but also compress them with different sizes and less storage space. At last, the reliability of the novel algorithm is assessed through a series of reconstruction and security performance tests. The results indicate that the encryption scheme has a better effect than encrypting one kind of image, and prove that the algorithm has high reconstruction accuracy and security.

Trompe-l’oeil Mirrors of the Soul in Jan David, S.J.’s Duodecim specula (Twelve Mirrors) of 1610

Abstract Jan David, S.J.’s Duodecim specula (Antwerp: Jan Moretus, 1610), an innovative emblematic treatise in twelve chapters, focuses on various kinds and degrees of specular image generated by the human soul. Each chapter responds to an opening imago, designed and engraved by Theodoor Galle, that illustrates the operations of the mirror in question. Three of the imagines, v. The Mirror of Others’ Eyes, viii. The Mirror of Created Things, and x. The Mirror of Example, rather than displaying persons, actions, or things that fall under the purview of the respective mirror, instead depict the mirrored image that such a speculum is seen to reflect. Accordingly, as printed imagines that prove upon closer inspection to contain specular imagines or, better, that function as pictorial representations of particular kinds of image, these imagines imaginum (images of images) can be said to produce a trompe-l’oeil effect. They ask the reader-viewer to consider why s/he thinks s/he sees a present image when what is actually seen by the eye is a pictured image, a pictured picture, doubly mediated by the process of representation. My essay examines how and why this deceptive effect was marshaled by David as a figure of thought: by articulating the manner and meaning of these three specula in particular, he offers the reader-viewer a therapeutic antidote wherewith to combat the human propensity for idolatry and self-deception.

A Corpus Analysis of Hate Speech in Pakistani Political Discourse on Twitter

This paper is a corpus-based quantitative study on language change that has arisen during national political events, such as the no-confidence movement against Imran Khan, the Prime Minister of Pakistan. This event, including many others, has given rise to the hate speech in Pakistani community online. In this paper, language change is discussed in terms of hate speech, collocation patterns, frequency of words (Nouns, Adjectives), N-Grams and Urdu hate speech words. A certain political time period is selected to make a comparison of the use of language and explore whether any variations have transpired during the political upheavals. These dynamics are explored because the frequency of hate speech words may suggest that how much aggressive the online political discourse has become. Moreover, it may also suggest that what kind of image is presented to the rest of the world through such usage of words. To find out the case, a data of 50,000 tweets was gathered using Tweetarchivist.com. This data was analyzed through sketch engine. The major findings from the research, reported in this paper, show that many of the tweets, through their collocations and frequency, contain hate speech elements which convey a negative image of the country across the globe.