Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

Database-driven businesses are using Big Data Analytics (BDA) to increase marketing efficacy as a result ‎of the boom in data generation brought about by the spread of digital technologies. This study examines the ‎use of BDA by well-known companies, including Amazon, Netflix, Coca-Cola, Nike, and H&M, to ‎accomplish strategic marketing goals like real-time engagement, predictive targeting, and customization. ‎The study examines several data processing methods, from machine learning and neural networks to ‎sentiment analysis and recommendation systems, using a comparative case-based methodology. It also ‎emphasizes the different business implications that are realized across sectors. In addition to being in line ‎with earlier theoretical frameworks, the research connects them to useful, real-world applications. Results ‎show that BDA dramatically increases ROI, conversion rates, and customer retention; yet, it also comes ‎with drawbacks including algorithmic bias, data privacy, and dynamic market adaptation. Finally, by offering ‎a thorough examination of BDA in marketing and highlighting the strategic significance of integrated, ‎moral, and technologically sophisticated data practices for long-term competitive advantage, this study ‎adds to the body of knowledge‎.

Explainability in artificial intelligence (AI) is an essential factor for building transparent, trustworthy, and ethical systems, particularly in ‎high-stakes domains such as healthcare, finance, justice, and autonomous systems. This study examines the foundations of AI explainability, ‎its critical role in fostering trust, and the current methodologies used to interpret AI models, such as post-hoc techniques, intrinsically inter-‎pretable models, and hybrid approaches. Despite these advancements, challenges persist, including trade-offs between accuracy and inter-‎pretability, scalability, ethical risks, and transparency gaps. The paper explores emerging trends like causality-based explanations, neuro-‎symbolic AI, and personalized frameworks, while emphasizing the integration of ethics and the need for automation in explainability. Future ‎directions stress the importance of collaboration among researchers, practitioners, and policymakers to establish industry standards and ‎regulations, ensuring that AI systems align with societal values and expectations.

This research presents an optimized design of a 2-to-4 decoder in Quantum-dot Cellular Automata (QCA) using a new formula developed from the Reversible. The proposed QCA decoder architecture employs 112 cells and occupies an area of 0.13μm², achieving a significant reduction in size compared to previous designs. The circuit is designed using a single-layer approach, enhancing its efficiency and minimizing power dissipation. Simulation results using the QCA Designer simulator version 2.0.3 demonstrate the enhanced functionality of the suggested decoder in regard to functionality and also efficiency, making it a promising candidate for future nanoscale integrated circuit applications.

This paper is concerned with a few misleading issues which are widespread in scientific literature. With a view to getting rid of the ambiguities present in the traditional literature regarding the said issues, proposed solution to each of those issues has been offered. The author feels that with a view to deepening thought and understanding as well as to upgrade the relevant literature at undergraduate level, it is a high time to consider immediate implementation of the present proposal in regard to the pertinent issues raised in this contribution.

This study is designed to determine the inheritance pattern of egg weight and egg shell weight of exotic laying chickens at 25, 51, 72 weeks and overall mean ages of the birds. For this purpose, thirty eggs were collected daily from the birds continuously for five days of egg production, at each ages 25, 51 and 72 weeks. The total number of eggs collected at each age was 150 and 450 for the total of three age periods. The mean values of egg weight and egg shell weight revealed an apparent increase from 25-72 weeks of age with their corresponding overall mean values. The least square means of egg shell weight at different age groups are significantly different at (P<0.01) while egg weight was significantly the same since egg shell weight depends on age variance for adequate performance. Statistical analytical system was used to obtain the variance components for the estimation of heritability. High and moderate heritability estimates were obtained when the age variance were included in the computation at different age groups for both traits while high, moderate and low estimates were obtained when the age variances were excluded. The heritability estimates from different egg quality traits were low to high. Since egg weight yielded high estimates at various age groups, the low and moderate heritability estimates recorded for egg shell weight imply that collection of additional records and improvement of non-genetic factors influencing the trait will improve the accuracy of characterizing the inherent ability of the birds.

Impact of wrong sexual behavior of adolescent has been surveyed. It effect on the victims has been proven negative, with consequences like health effect including financial effect been a drawback, acting like a barrier to the ideal development of an individual adolescent. Wrong sexual behavior has been found to be on the increase, forcing adolescents to drop-out of school, exhibiting behaviors that do not conform to the development of the society. Result of this research findings shows the impact of wrong sexual behavior to include: Disregard for cultural/societal moral values, teenage/adolescent pregnancies, rapid increase in STDs and HIV/AIDS transmission, eagerness to practices illicit sexual acts, wrong conception about the opposite sex including sexual intercourse addiction with prostitution tendencies. The high positive percentages achieved from statistical analysis (respondent view-point) presents these impact as been on the increase, capable of dragging the society towards ruin. Recommendations of this study presents a view that the problem of adolescent wrong sexual behavior can be control , presenting guides like proper guidance and counseling , proper monitoring, aggressive sex education, reawakening of traditional African moral and value teachings, inclusion of sex education in religious teachings including proper dressing and restricting of adolescents from pornographic materials.

A survey on economics of Maize (Zea mays L.) production in Nigeria and its traditional utilization was conducted. Result of the survey presented maize having the potential of increasing the economy of the nation. Analyzed data presented maize been able to increase the income of the local people and foreign exchange earnings, as observed where price of the crop during 1980 (2500) stands below price at 1989 (14000). Increased in land utilization with increase in years was observed in data analyzed as 1980 (95600 hectares) of land area stands below (211740) in1989. Traditional utilization of maize has been found to include where the crop is been used as herbal medicine, as food, in traditional religion, in-addition to where the crop is been utilized in marital rituals.

Purpose: The 2014 Ebola epidemic resulted in 26,683 cases and 11,022 deaths by May 6th, 2015. With first responders deploying from the United States to assist with the many challenges being faced in the field, they encountered a new set of traumatic events and situations that undoubtedly put them at risk for developing a mental disorder. Organizations must be prepared to provide mental health services for their employees and volunteers once they return.Approach: The authors questioned the potential prevalence of PTSD, ASD, and depression among returning first responders and which therapy method would be the most effective in terms of an individual’s recovered or improved condition. This was done through the use of AnyLogic® 7.0. An agent-based method to model the stress levels a first responder may experience while dealing with Ebola was used, where all responders begin in the healthy state and can develop mental health disorders. The therapy options tested were Cognitive Processing Therapy, Prolonged Exposure, and Group Based Exposure Therapy.Results: GBET for PTSD patients provided the most positive results in terms of condition recovery and improvement.Conclusion: The results showed evidence that the type of therapy used can drastically affect the individual’s mental health outcome.

Muntjibpur pond was studied to find out the conservation status of the fishes naturally occurring in it. The survey was conducted from July 2014 to June 2015. During exploration, a total of 13 species of fishes belonging to 12 genera, 8 families and 5 orders were identified. As per latest version of IUCN Red List, out of 13 species of fishes identified, 1 species comes under UV (vulnerable), 1 under NT (near threatened), 8 under LC (least concern) and 3 species are NE (not evaluated) so far. No fish species identified here comes under EN (endangered) category.