Baghdad College for Economic Sciences University

Artificial intelligence and machine learning technologies have evolved many fields in authorship analysis and plagiarism detection. AI is also being applied to probe into authorship and plagiarism in writing; “AI-Driven Insights into Authorial Style and Plagiarism Detection,” explains how sharp algorithms can analyze, comprehend and compare writing patterns to yield deeper insights into identity, stylistic fingerprints and even content plagiarism. The analysis of authorship was consisted of a manual analysis process that is a time-consuming or a statistical models application that was limited in scope and/or resource utilization. Text directed systems in the new era of artificial intelligence are able to consume and process vast amounts of text and rapidly locate minor stylistic cues, linguistic fingerprints and syntactic styles that are specific to specific writers. They are natural language processing, stylometry and deep learning models that analyze the patterns of lexical choice, sentence structure, semantic preference and rhythm of expression of an author. Reactive and adjustable in its character - this is what leads to one of the principal advantages of AI in this case. Machine learning algorithm classes may be trained on the known corpus of an author in order to create a signature style profile, and analyze new or controversial texts. These techniques are especially effective in the area of identifying ghostwriting and forgers who present academic papers as well as other literature related disputes concerning authorship. The training information is not merely a reference to a specific bit of information. Such effective detection of plagiarism done by AI based plagiarism detectors is much better and is more pervasive as compared to the conventional method where keywords are used. Nowadays, AI systems can be used to detect paraphrased sentences, translated plagiarism or cross-lingual semantic similarity with the help of advanced technologies (semantic analysis, neural network or transformers-based language models like BERT or GPT). This creates powerful tools at the disposal of universities, publishers and legal authorities when it comes to protecting academic and artistic integrity. Moreover, ethical aspects of the consequences of such developments have also been of course posed as an issue. And although AI has already reached quite human-like writing, it became a new age of isms, meaning works that are written in the style of a certain author can be used to forge or invent books. Therefore, to the extent that the tools that will be developed by AI have the potential to be life changing in the field, it must be coupled with preventative measures and a code of ethics in its development to help guard against the usurpation of its power.

The research aims to measure the correlation between the use of social responsibility in the bank sample of the research and its impact in achieving the three dimensions of sustainable development, because of its role in developing new methods followed by banks to achieve their economic, social, and environmental goals. The statistical results were described and analyzed through the data collected through the questionnaire at the level of the research sample represented by the Iraqi Investment Bank, and based on the level of availability of the selected sample and its relationship to the research variables, where (arithmetic mean and standard deviation) were employed for this purpose, through the statistical package. (SPSS V.23). One of the most prominent findings of the research was that there was a strong correlation between the use of social responsibility accounting in the Iraqi Investment Bank and the economic dimension (.879), the social dimension (.831), and the environmental dimension (.854), in addition to the presence of awareness Distinguished by the employees and employees of the bank for the use of social responsibility and the importance of its role in achieving the dimensions of sustainable development for future generations.

This article proposes a portable radar system combining multiple sensors to enhance monitoring capabilities in various applications. Servo motors enable directional control, while ultrasonic sensors enable precise distance measurements. Also, environmental awareness can be enhanced by the combination of temperature sensors (DHT22), light sensors (LDR), gas sensors (MQ-2), and a camera module, which will be helpful as we can see the images associated with the data. GPS is used to find the location, which maps to the actual coordinates of the sensor. This article concluded that these devices could be used to design an advanced radar system and transmit data in real-time.

The investigation delves into the concept of inferences in the use of the English language, with a particular emphasis on pragmatics and Grice's maxims. It underscores the significance of inferences in comprehending communicative intent, particularly during interviews. The objective of the research is to stimulate further investigation of inference in related studies, thereby providing valuable insights for English language users and linguistics students. Furthermore, the analysis of "The Dr. Phil Show" investigates its influence on the representation of psychotherapy and popular culture. The paper conducted a statistical analysis of the types and frequencies of questions in seven texts from Dr. Phil's TV program to reveal strategies employed to obscure reality. The focus was on interviewee responses. The study distinguished between live and edited segments by examining 80 episodes, thereby advancing the cultivation of research and improving comprehension of psychotherapy practices. Additionally, it addresses potential misrepresentations in media portrayals. The results emphasise the importance of precise depictions of therapy in influencing the opinions of viewers

The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, including unauthorized access, data manipulation, and privacy vulnerabilities. Traditional IoT security mechanisms, such as encryption and intrusion detection systems, often struggle to provide comprehensive protection due to centralized architectures and scalability limitations. This study explores blockchain technology as a potential solution to enhance IoT security by leveraging its decentralized, tamper-resistant framework. The primary objective is to evaluate the effectiveness of blockchain in mitigating IoT security vulnerabilities while analyzing its impact on network performance, including latency, computational overhead, and energy consumption. The research employs a combination of qualitative and quantitative methods, including case studies and experimental simulations, to assess blockchain's role in securing IoT networks. Findings indicate that blockchain significantly improves authentication success rates, data integrity, and encryption efficiency, reducing the risk of cyber threats. However, the study also highlights trade-offs, such as increased latency and energy consumption, which may affect real-time IoT applications. The results emphasize the need for further optimization of blockchain architectures and the exploration of hybrid security models incorporating artificial intelligence and energy-efficient consensus mechanisms. This research contributes to the ongoing development of secure and scalable IoT solutions by providing a comprehensive analysis of blockchain's strengths and limitations in safeguarding IoT ecosystems.